Mathematical and statistical software Books

Book SynopsisModern Analysis of Customer Surveys: with applications using R Customer survey studies deal with customer, consumer and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays. This book demonstrates how integrating such basic analysis with more advanced tools, provides insights into non-obvious patterns and important relationships between the survey variables. This knowledge can significantly affect the conclusions derived from a survey. Key features: Provides an integrated case studies-based approach to analysing customer survey data. Presents a general introduction to customer surveys, within an organization's business cycle. Contains classical techniques with modern and non standard tools.Table of ContentsForeword xvii Preface xix Contributors xxiii Part I Basic Aspects of Customer Satisfaction Survey Data Analysis 1 Standards and Classical Techniques in Data Analysis of Customer Satisfaction Surveys 3 Silvia Salini and Ron S. Kenett 1.1 Literature on customer satisfaction surveys 4 1.2 Customer satisfaction surveys and the business cycle 4 1.3 Standards used in the analysis of survey data 7 1.4 Measures and models of customer satisfaction 12 1.4.1 The conceptual construct 12 1.4.2 The measurement process 13 1.5 Organization of the book 15 1.6 Summary 17 References 17 2 The ABC Annual Customer Satisfaction Survey 19 Ron S. Kenett and Silvia Salini 2.1 The ABC company 19 2.2 ABC 2010 ACSS: Demographics of respondents 20 2.3 ABC 2010 ACSS: Overall satisfaction 22 2.4 ABC 2010 ACSS: Analysis of topics 24 2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers 27 2.6 Summary 28 References 28 Appendix 29 3 Census and Sample Surveys 37 Giovanna Nicolini and Luciana Dalla Valle 3.1 Introduction 37 3.2 Types of surveys 39 3.2.1 Census and sample surveys 39 3.2.2 Sampling design 40 3.2.3 Managing a survey 40 3.2.4 Frequency of surveys 41 3.3 Non-sampling errors 41 3.3.1 Measurement error 42 3.3.2 Coverage error 42 3.3.3 Unit non-response and non-self-selection errors 43 3.3.4 Item non-response and non-self-selection error 44 3.4 Data collection methods 44 3.5 Methods to correct non-sampling errors 46 3.5.1 Methods to correct unit non-response errors 46 3.5.2 Methods to correct item non-response 49 3.6 Summary 51 References 52 4 Measurement Scales 55 Andrea Bonanomi and Gabriele Cantaluppi 4.1 Scale construction 55 4.1.1 Nominal scale 56 4.1.2 Ordinal scale 57 4.1.3 Interval scale 58 4.1.4 Ratio scale 59 4.2 Scale transformations 60 4.2.1 Scale transformations referred to single items 61 4.2.2 Scale transformations to obtain scores on a unique interval scale 66 Acknowledgements 69 References 69 5 Integrated Analysis 71 Silvia Biffignandi 5.1 Introduction 71 5.2 Information sources and related problems 73 5.2.1 Types of data sources 73 5.2.2 Advantages of using secondary source data 73 5.2.3 Problems with secondary source data 74 5.2.4 Internal sources of secondary information 75 5.3 Root cause analysis 78 5.3.1 General concepts 78 5.3.2 Methods and tools in RCA 81 5.3.3 Root cause analysis and customer satisfaction 85 5.4 Summary 87 Acknowledgement 87 References 87 6 Web Surveys 89 Roberto Furlan and Diego Martone 6.1 Introduction 89 6.2 Main types of web surveys 90 6.3 Economic benefits of web survey research 91 6.3.1 Fixed and variable costs 92 6.4 Non-economic benefits of web survey research 94 6.5 Main drawbacks of web survey research 96 6.6 Web surveys for customer and employee satisfaction projects 100 6.7 Summary 102 References 102 7 The Concept and Assessment of Customer Satisfaction 107 Irena Ograjenšek and Iddo Gal 7.1 Introduction 107 7.2 The quality–satisfaction–loyalty chain 108 7.2.1 Rationale 108 7.2.2 Definitions of customer satisfaction 108 7.2.3 From general conceptions to a measurement model of customer satisfaction 110 7.2.4 Going beyond SERVQUAL: Other dimensions of relevance to the B2B context 112 7.2.5 From customer satisfaction to customer loyalty 113 7.3 Customer satisfaction assessment: Some methodological considerations 115 7.3.1 Rationale 115 7.3.2 Think big: An assessment programme 115 7.3.3 Back to basics: Questionnaire design 116 7.3.4 Impact of questionnaire design on interpretation 118 7.3.5 Additional concerns in the B2B setting 119 7.4 The ABC ACSS questionnaire: An evaluation 119 7.4.1 Rationale 119 7.4.2 Conceptual issues 119 7.4.3 Methodological issues 120 7.4.4 Overall ABC ACSS questionnaire asssessment 121 7.5 Summary 121 References 122 Appendix 126 8 Missing Data and Imputation Methods 129 Alessandra Mattei, Fabrizia Mealli and Donald B. Rubin 8.1 Introduction 129 8.2 Missing-data patterns and missing-data mechanisms 131 8.2.1 Missing-data patterns 131 8.2.2 Missing-data mechanisms and ignorability 132 8.3 Simple approaches to the missing-data problem 134 8.3.1 Complete-case analysis 134 8.3.2 Available-case analysis 135 8.3.3 Weighting adjustment for unit nonresponse 135 8.4 Single imputation 136 8.5 Multiple imputation 138 8.5.1 Multiple-imputation inference for a scalar estimand 138 8.5.2 Proper multiple imputation 139 8.5.3 Appropriately drawing imputations with monotone missing-data patterns 140 8.5.4 Appropriately drawing imputations with nonmonotone missing-data patterns 141 8.5.5 Multiple imputation in practice 142 8.5.6 Software for multiple imputation 143 8.6 Model-based approaches to the analysis of missing data 144 8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example 145 8.8 Summary 149 Acknowledgements 150 References 150 9 Outliers and Robustness for Ordinal Data 155 Marco Riani, Francesca Torti and Sergio Zani 9.1 An overview of outlier detection methods 155 9.2 An example of masking 157 9.3 Detection of outliers in ordinal variables 159 9.4 Detection of bivariate ordinal outliers 160 9.5 Detection of multivariate outliers in ordinal regression 161 9.5.1 Theory 161 9.5.2 Results from the application 163 9.6 Summary 168 References 168 Part II Modern Techniques in Customer Satisfaction Survey Data Analysis 10 Statistical Inference for Causal Effects 173 Fabrizia Mealli, Barbara Pacini and Donald B. Rubin 10.1 Introduction to the potential outcome approach to causal inference 173 10.1.1 Causal inference primitives: Units, treatments, and potential outcomes 175 10.1.2 Learning about causal effects: Multiple units and the stable unit treatment value assumption 176 10.1.3 Defining causal estimands 177 10.2 Assignment mechanisms 179 10.2.1 The criticality of the assignment mechanism 179 10.2.2 Unconfounded and strongly ignorable assignment mechanisms 180 10.2.3 Confounded and ignorable assignment mechanisms 181 10.2.4 Randomized and observational studies 181 10.3 Inference in classical randomized experiments 182 10.3.1 Fisher’s approach and extensions 183 10.3.2 Neyman’s approach to randomization-based inference 183 10.3.3 Covariates, regression models, and Bayesian model-based inference 184 10.4 Inference in observational studies 185 10.4.1 Inference in regular designs 186 10.4.2 Designing observational studies: The role of the propensity score 186 10.4.3 Estimation methods 188 10.4.4 Inference in irregular designs 188 10.4.5 Sensitivity and bounds 189 10.4.6 Broken randomized experiments as templates for the analysis of some irregular designs 189 References 190 11 Bayesian Networks Applied to Customer Surveys 193 Ron S. Kenett, Giovanni Perruca and Silvia Salini 11.1 Introduction to Bayesian networks 193 11.2 The Bayesian network model in practice 197 11.2.1 Bayesian network analysis of the ABC 2010 ACSS 197 11.2.2 Transport data analysis 201 11.2.3 R packages and other software programs used for studying BNs 210 11.3 Prediction and explanation 211 11.4 Summary 213 References 213 12 Log-linear Model Methods 217 Stephen E. Fienberg and Daniel Manrique-Vallier 12.1 Introduction 217 12.2 Overview of log-linear models and methods 218 12.2.1 Two-way tables 218 12.2.2 Hierarchical log-linear models 220 12.2.3 Model search and selection 222 12.2.4 Sparseness in contingency tables and its implications 223 12.2.5 Computer programs for log-linear model analysis 223 12.3 Application to ABC survey data 224 12.4 Summary 227 References 228 13 CUB Models: Statistical Methods and Empirical Evidence 231 Maria Iannario and Domenico Piccolo 13.1 Introduction 231 13.2 Logical foundations and psychological motivations 233 13.3 A class of models for ordinal data 233 13.4 Main inferential issues 236 13.5 Specification of CUB models with subjects’ covariates 238 13.6 Interpreting the role of covariates 240 13.7 A more general sampling framework 241 13.7.1 Objects’ covariates 241 13.7.2 Contextual covariates 243 13.8 Applications of CUB models 244 13.8.1 Models for the ABC annual customer satisfaction survey 245 13.8.2 Students’ satisfaction with a university orientation service 246 13.9 Further generalizations 248 13.10 Concluding remarks 251 Acknowledgements 251 References 251 Appendix 255 A program in R for CUB models 255 A.1 Main structure of the program 255 A.2 Inference on CUB models 255 A.3 Output of CUB models estimation program 256 A.4 Visualization of several CUB models in the parameter space 257 A.5 Inference on CUB models in a multi-object framework 257 A.6 Advanced software support for CUB models 258 14 The Rasch Model 259 Francesca De Battisti, Giovanna Nicolini and Silvia Salini 14.1 An overview of the Rasch model 259 14.1.1 The origins and the properties of the model 259 14.1.2 Rasch model for hierarchical and longitudinal data 263 14.1.3 Rasch model applications in customer satisfaction surveys 265 14.2 The Rasch model in practice 267 14.2.1 Single model 267 14.2.2 Overall model 268 14.2.3 Dimension model 272 14.3 Rasch model software 277 14.4 Summary 278 References 279 15 Tree-based Methods and Decision Trees 283 Giuliano Galimberti and Gabriele Soffritti 15.1 An overview of tree-based methods and decision trees 283 15.1.1 The origins of tree-based methods 283 15.1.2 Tree graphs, tree-based methods and decision trees 284 15.1.3 CART 287 15.1.4 CHAID 293 15.1.5 PARTY 295 15.1.6 A comparison of CART, CHAID and PARTY 297 15.1.7 Missing values 297 15.1.8 Tree-based methods for applications in customer satisfaction surveys 298 15.2 Tree-based methods and decision trees in practice 300 15.2.1 ABC ACSS data analysis with tree-based methods 300 15.2.2 Packages and software implementing tree-based methods 303 15.3 Further developments 304 References 304 16 PLS Models 309 Giuseppe Boari and Gabriele Cantaluppi 16.1 Introduction 309 16.2 The general formulation of a structural equation model 310 16.2.1 The inner model 310 16.2.2 The outer model 312 16.3 The PLS algorithm 313 16.4 Statistical interpretation of PLS 319 16.5 Geometrical interpretation of PLS 320 16.6 Comparison of the properties of PLS and LISREL procedures 321 16.7 Available software for PLS estimation 323 16.8 Application to real data: Customer satisfaction analysis 323 References 329 17 Nonlinear Principal Component Analysis 333 Pier Alda Ferrari and Alessandro Barbiero 17.1 Introduction 333 17.2 Homogeneity analysis and nonlinear principal component analysis 334 17.2.1 Homogeneity analysis 334 17.2.2 Nonlinear principal component analysis 336 17.3 Analysis of customer satisfaction 338 17.3.1 The setting up of indicator 338 17.3.2 Additional analysis 340 17.4 Dealing with missing data 340 17.5 Nonlinear principal component analysis versus two competitors 343 17.6 Application to the ABC ACSS data 344 17.6.1 Data preparation 344 17.6.2 The homals package 345 17.6.3 Analysis on the ‘complete subset’ 346 17.6.4 Comparison of NLPCA with PCA and Rasch analysis 350 17.6.5 Analysis of ‘entire data set’ for the comparison of missing data treatments 352 17.7 Summary 355 References 355 18 Multidimensional Scaling 357 Nadia Solaro 18.1 An overview of multidimensional scaling techniques 357 18.1.1 The origins of MDS models 358 18.1.2 MDS input data 359 18.1.3 MDS models 362 18.1.4 Assessing the goodness of MDS solutions 369 18.1.5 Comparing two MDS solutions: Procrustes analysis 371 18.1.6 Robustness issues in the MDS framework 371 18.1.7 Handling missing values in MDS framework 373 18.1.8 MDS applications in customer satisfaction surveys 373 18.2 Multidimensional scaling in practice 374 18.2.1 Data sets analysed 375 18.2.2 MDS analyses of overall satisfaction with a set of ABC features: The complete data set 375 18.2.3 Weighting objects or items 381 18.2.4 Robustness analysis with the forward search 382 18.2.5 MDS analyses of overall satisfaction with a set of ABC features: The incomplete data set 383 18.2.6 Package and software for MDS methods 384 18.3 Multidimensional scaling in a future perspective 386 18.4 Summary 386 References 387 19 Multilevel Models for Ordinal Data 391 Leonardo Grilli and Carla Rampichini 19.1 Ordinal variables 391 19.2 Standard models for ordinal data 393 19.2.1 Cumulative models 394 19.2.2 Other models 395 19.3 Multilevel models for ordinal data 395 19.3.1 Representation as an underlying linear model with thresholds 396 19.3.2 Marginal versus conditional effects 397 19.3.3 Summarizing the cluster-level unobserved heterogeneity 397 19.3.4 Consequences of adding a covariate 398 19.3.5 Predicted probabilities 399 19.3.6 Cluster-level covariates and contextual effects 399 19.3.7 Estimation of model parameters 400 19.3.8 Inference on model parameters 401 19.3.9 Prediction of random effects 402 19.3.10 Software 403 19.4 Multilevel models for ordinal data in practice: An application to student ratings 404 References 408 20 Quality Standards and Control Charts Applied to Customer Surveys 413 Ron S. Kenett, Laura Deldossi and Diego Zappa 20.1 Quality standards and customer satisfaction 413 20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction 414 20.3 Control Charts and ISO 7870 417 20.4 Control charts and customer surveys: Standard assumptions 420 20.4.1 Introduction 420 20.4.2 Standard control charts 420 20.5 Control charts and customer surveys: Non-standard methods 426 20.5.1 Weights on counts: Another application of the c chart 426 20.5.2 The χ2 chart 427 20.5.3 Sequential probability ratio tests 428 20.5.4 Control chart over items: A non-standard application of SPC methods 429 20.5.5 Bayesian control chart for attributes: A modern application of SPC methods 432 20.5.6 Control chart for correlated Poisson counts: When things become fairly complicated 433 20.6 The M-test for assessing sample representation 433 20.7 Summary 435 References 436 21 Fuzzy Methods and Satisfaction Indices 439 Sergio Zani, Maria Adele Milioli and Isabella Morlini 21.1 Introduction 439 21.2 Basic definitions and operations 440 21.3 Fuzzy numbers 441 21.4 A criterion for fuzzy transformation of variables 443 21.5 Aggregation and weighting of variables 445 21.6 Application to the ABC customer satisfaction survey data 446 21.6.1 The input matrices 446 21.6.2 Main results 448 21.7 Summary 453 References 455 Appendix an Introduction to R 457 Stefano Maria Iacus A.1 Introduction 457 A.2 How to obtain R 457 A.3 Type rather than ‘point and click’ 458 A.3.1 The workspace 458 A.3.2 Graphics 458 A.3.3 Getting help 459 A.3.4 Installing packages 459 A.4 Objects 460 A.4.1 Assignments 460 A.4.2 Basic object types 462 A.4.3 Accessing objects and subsetting 466 A.4.4 Coercion between data types 469 A.5 S4 objects 470 A.6 Functions 472 A.7 Vectorization 473 A.8 Importing data from different sources 475 A.9 Interacting with databases 476 A.10 Simple graphics manipulation 477 A.11 Basic analysis of the ABC data 481 A.12 About this document 496 A.13 Bibliographical notes 496 References 496 Index 499

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Book SynopsisThe information contained in this book has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses in the General Linear Model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression of ANOVA than do traditional sums of squares and scalar equations. The book contains a balanced treatment of regression and ANOVA yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text contains almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided, and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented. All exercises involve only real data, collected in the course of scientific research. The book is divided Trade Review“…very useful to applied scientists and for graduate level courses in areas of non-mathematical statistics…” (Zentralblatt Math, Vol.1039, No.8, 2004)Table of ContentsPreface. Examples and Limits of the GLM. Statement of the Model, Estimation, and Testing. Some Distributions for the GLM. Multiple Regression: General Considerations. Testing Hypotheses in Multiple Regression. Correlations. GLM Assumption Diagnostics. GLM Computation Diagnostics. Polynomial Regression. Transformations. Selecting the Best Model. Coding Schemes for Regression. One-Way ANOVA. Complete, Two-Way Factorial ANOVA. Special Cases of Two-Way ANOVA and Random Effects Basics. The Full Model in Every Cell (ANCOVA as a Special Case). Understanding and Computing Power for the GLM. Appendix A. Matrix Algebra for Linear Models. Appendix B. Statistical Tables. Appendix C. Study Guide for Linear Model Theory. Appendix D. Homework and Example Data. Appendix E. Introduction to SAS/IML. Appendix F. A Brief Manual to LINMOD. Appendix G. SAS/IML Power Program User's Guide. Appendix H. Regression Model Selection Data. References. Index.

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Book SynopsisOffers coverage of basic and advanced statistical methods, concentrating on graphical inspection, and featuring step-by-step instruction to help non-statisticians understand the methodology.Trade Review"...suitable as a reference book for experienced statisticians, a vehicle for learning the S statistical computing language, or a resource for statistics instructors..." (The American Statistician, Vol. 58, No. 1, February 2004) "...especially useful as an introduction to a wide variety of data analysis techniques." (R News) "...The book is well written - there is an air of common sense throughout - and is at a level which ensures its usefulness for a wide range of readers..." (Zentralblatt Math, Vol. 1001, No.01, 2003) "...the book is a useful and practical introduction to many areas of statistical data analysis." (Computational STatistics & Data Analysis) "...surely not the last statistics book you’ll ever need, but it might well be the first you will ever really use." (Basic Applied Ecology, Vol. 4, No. 3) "...recommended...contains a wealth of sage advice..." (Technometrics, Vol. 45, No. 4, November 2003) “...a practical introduction to statistics...does not cover all...sophisticated statistical and graphical features of the S-Plus system, but provides a first class starting point—and, probably, for most readers, a sufficient end point.” (Quarterly of Applied Mathematics, LXI, No. 4, December 2003) “…a valiant and useful first attempt to present both statistics and S-PLUS together…” (Journal of The Royal Statistical Society Vol.167 No.4) Table of ContentsStatistical methods Introduction to S-Plus Experimental design Central tendency Probability Variance The Normal distribution Power calculations Understanding data: graphical analysis Understanding data: tabular analysis Classical tests Bootstrap and jackknife Statistical models in S-Plus Regression Analysis of variance Analysis of covariance Model criticism Contrasts Split-plot Anova Nested designs and variance components analysis Graphs, functions and transformations Curve fitting and piecewise regression Non-linear regression Multiple regression Model simplification Probability distributions Generalised linear models Proportion data: binomial errors Count data: Poisson errors Binary response variables Tree models Non-parametric smoothing Survival analysis Time series analysis Mixed effects models Spatial statistics Bibliography Index

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Book SynopsisA how to guide for applying statistical methods to biomarker data analysis Presenting a solid foundation for the statistical methods that are used to analyze biomarker data, Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation. The authors provide descriptions of select elementary statistical methods that are traditionally used to analyze biomarker data with a focus on the proper application of each method, including necessary assumptions, software recommendations, and proper interpretation of computer output. In addition, the book discusses frequently encountered challenges in analyzing biomarker data and how to deal with them, methods for the quality assessment of biomarkers, and biomarker study designs. Covering a broad range of statistical methods that have been used to analyze biomarker data in published research studies, Analysis of Biomarker Data: A Practical Guide also features: ATable of ContentsPreface xiii Acknowledgements xvii 1 Introduction 1 1.1 What is a Biomarker? 1 1.2 Biomarkers Versus Surrogate Endpoints 2 1.3 Organization of This Book 3 2 Designing Biomarker Studies 5 2.1 Introduction 5 2.2 Designing the Study 6 2.2.1 The Exposure–Disease Association 6 2.2.2 Cross-sectional Studies 7 2.2.3 Case–Control Studies 7 2.2.4 Retrospective Cohort Studies 9 2.2.5 Prospective Cohort Studies 9 2.2.6 Observational Studies 10 2.2.7 Randomized Controlled Trials 11 2.3 Designing the Analysis 13 2.3.1 Choosing the Appropriate Measure of Association 15 2.3.1.1 Odds Ratio versus Risk Ratio 15 2.3.1.2 Consequences of Not Choosing the Appropriate Measure of Association 16 2.3.2 Choosing the Appropriate Statistical Analysis 16 2.3.3 Choosing the Appropriate Sample Size 17 2.4 Presenting Statistical Results 18 Problems 20 3 Elementary Statistical Methods for Analyzing Biomarker Data 21 3.1 Introduction 21 3.2 Graphical and Tabular Summaries 21 3.3 Descriptive Statistics 26 3.4 Describing the Shape of Distributions 31 3.5 Sampling Distributions 33 3.6 Introduction to Statistical Inference 34 3.6.1 Point Estimation and Confidence Interval Estimation 34 3.6.2 Hypothesis Testing 38 3.7 Comparing Means Across Groups 43 3.7.1 Two Group Comparisons 44 3.7.2 Multiple-Group Comparisons 45 3.8 Correlation Analysis 50 3.9 Regression Analysis 52 3.9.1 Simple Linear Regression 52 3.9.2 Multiple Regression 55 3.9.3 Analysis of Covariance 58 3.10 Analyzing Cross-Classified Data 61 3.10.1 Testing for Independence 61 3.10.2 Comparison of Proportions 65 Problems 69 4 Frequently Encountered Challenges in Analyzing Biomarker Data and How to Deal with Them 72 4.1 Introduction 72 4.2 Non-Normally Distributed Data 73 4.2.1 The Effects of Non-Normality 73 4.2.2 Testing Distributional Assumptions 74 4.2.2.1 Graphical Methods for Assessing Normality 74 4.2.2.2 Measures of Skewness and Kurtosis 81 4.2.2.3 Formal Hypothesis Tests of the Normality Assumption 83 4.2.3 Remedial Measures for Violation of a Distributional Assumption 86 4.2.3.1 Choosing a Transformation 86 4.2.3.2 Using a Robust Statistical Procedure 92 4.2.3.3 Distribution-Free Alternatives 93 4.3 Heterogeneity of Variance 113 4.3.1 The Effects of Heterogeneity 113 4.3.2 The Importance of Heterogeneity in the Comparison of Means 113 4.3.2.1 Comparisons of Two Groups 113 4.3.2.2 Comparisons of More Than Two Groups 116 4.3.2.3 Multiple Comparisons 118 4.4 Dependent Groups 122 4.4.1 The Consequences of Ignoring Dependence Among Groups 122 4.4.2 Comparing Two Dependent Means 124 4.4.2.1 Paired t-test 124 4.4.2.2 Wilcoxon Signed Ranks Test 127 4.4.2.3 Sign Test 128 4.4.3 Tests of Dependent Proportions 134 4.4.3.1 McNemar’s Test 134 4.4.3.2 Cochran’s Q test 138 4.4.3.3 Sample Size and Power Considerations 142 4.5 Correlated Outcomes 144 4.5.1 Choosing the Appropriate Measure of Association 144 4.5.1.1 Spearman’s rho 144 4.5.1.2 Kendall’s tau-b 146 4.5.2 Recommended Methods of Statistical Analysis for Correlation Coefficients 148 4.5.3 Recommended Methods for Interpreting Correlation Coefficient Results 156 4.5.4 Sample Size Issues in Correlation Analysis 157 4.5.5 Comparison of Correlation Coefficients 171 4.5.5.1 Comparison of Independent Correlation Coefficients 172 4.5.5.2 Comparison of Dependent Correlation Coefficients 174 4.5.6 Sample Size Issues When Comparing Two Correlation Coefficients 181 4.5.6.1 Sample Size Issues When Comparing Independent Correlation Coefficients 181 4.5.6.2 Sample Size Issues When Comparing Dependent Correlation Coefficients 183 4.6 Clustered Data 184 4.7 Outliers 199 4.7.1 The Effects of Outliers 199 4.7.2 Detection of Outliers 199 4.7.3 Methods for Accommodating Outliers 207 4.8 Limits of Detection and Non-Detected Observations 208 4.8.1 Statistical Inference When NDs Are Present 210 4.8.2 Maximum Likelihood Estimation of a Correlation Coefficient When Both X and Y Are Subject to Non-Detects 210 4.8.3 Comparison of Confidence Interval Methods for Correlation Coefficients When Both Variables Are Subject to Limits of Detection 212 4.9 The Analysis of Cross-Classified Categorical Data 221 4.9.1 Choosing the Appropriate Measure of Association 221 4.9.1.1 The Odds Ratio 221 4.9.1.2 Risk Ratio 223 4.9.1.3 Risk Difference 224 4.9.1.4 Odds Ratio for Paired Data 225 4.9.2 Choosing the Appropriate Statistical Analysis 225 4.9.3 Choosing the Appropriate Sample Size 226 4.9.4 Choosing a Statistical Method When Both the Predictor and the Outcome Are Dichotomous 226 4.9.4.1 Comparing Two Independent Groups in Terms of a Binomial Proportion 226 4.9.4.2 Exact Test for Independence of Rows and Columns in a 2 × 2 Table 230 4.9.4.3 Exact Inference for Odds Ratios 232 4.9.4.4 Inference for the Odds Ratio for Paired Data 234 4.9.5 Choice of a Statistical Method When the Predictor is Ordinal and the Outcome is Dichotomous 237 4.9.5.1 Tests for a Significant Trend in Proportions 237 4.9.6 Choice of a Statistical Method When Both the Predictor and the Outcome are Ordinal 240 4.9.6.1 Test for Linear-by-Linear Association 240 4.9.7 Choice of a Statistical Method When Both the Predictor and the Outcome are Nominal 243 4.9.7.1 Fisher–Freeman–Halton Test 243 Problems 246 5 Validation of Biomarkers 255 5.1 Overview of Methods for Assessing Characteristics of Biomarkers 255 5.2 General Description of Measures of Agreement 257 5.2.1 Discrete Variables 257 5.2.1.1 Cohen’s Kappa 257 5.2.1.2 Extensions of Coefficient Kappa 265 5.2.1.3 Weighted Kappa 273 5.2.2 Continuous Variables 275 5.2.2.1 Pearson’s Correlation Coefficient 275 5.2.2.2 Alternatives to Pearson’s Correlation Coefficient 277 5.3 Assessing Reliability of a Biomarker 287 5.3.1 General Considerations 287 5.3.2 Assessing Reliability of a Dichotomous Biomarker 287 5.3.2.1 Dichotomous Biomarker, More Than Two Raters 289 5.3.3 Assessing Reliability of a Continuous Biomarker 291 5.3.4 Assessing Inter-Subject, Intra-Subject, and Analytical Measurement Variability 292 5.4 Assessing Validity 294 5.4.1 General Considerations 294 5.4.2 Assessing Validity When a Gold Standard is Available 295 5.4.2.1 Dichotomous Biomarkers 295 5.4.2.2 Comparing Several Dichotomous Biomarkers 302 5.4.2.3 Continuous Biomarkers 304 5.4.3 Assessing Validity When a Gold Standard is Not Available 314 5.4.3.1 Dichotomous Biomarkers 315 5.4.3.2 Continuous Biomarkers 319 5.4.4 Assessing Criterion Validity in Method Comparison Studies 328 5.4.5 Assessing Construct Validity in Method Comparison Studies 329 Problems 329 References 332 Solutions to Problems 348 Index 391

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Book SynopsisNonlinear Parameter Optimization Using R John C.Trade Review"The book chapters are enriched by little anecdotes, and the reader obviously benefits from John C. Nash's experience of more than 30 years in the field of nonlinear optimization. This experience translates into many practical recommendations and tweaks. The book provides plenty of code examples and useful code snippets." (Biometrical Journal, 2016)Table of ContentsPreface xv 1 Optimization problem tasks and how they arise 1 1.1 The general optimization problem 1 1.2 Why the general problem is generally uninteresting 2 1.3 (Non-)Linearity 4 1.4 Objective function properties 4 1.4.1 Sums of squares 4 1.4.2 Minimax approximation 5 1.4.3 Problems with multiple minima 5 1.4.4 Objectives that can only be imprecisely computed 5 1.5 Constraint types 5 1.6 Solving sets of equations 6 1.7 Conditions for optimality 7 1.8 Other classifications 7 References 8 2 Optimization algorithms – an overview 9 2.1 Methods that use the gradient 9 2.2 Newton-like methods 12 2.3 The promise of Newton’s method 13 2.4 Caution: convergence versus termination 14 2.5 Difficulties with Newton’s method 14 2.6 Least squares: Gauss–Newton methods 15 2.7 Quasi-Newton or variable metric method 17 2.8 Conjugate gradient and related methods 18 2.9 Other gradient methods 19 2.10 Derivative-free methods 19 2.10.1 Numerical approximation of gradients 19 2.10.2 Approximate and descend 19 2.10.3 Heuristic search 20 2.11 Stochastic methods 20 2.12 Constraint-based methods – mathematical programming 21 References 22 3 Software structure and interfaces 25 3.1 Perspective 25 3.2 Issues of choice 26 3.3 Software issues 27 3.4 Specifying the objective and constraints to the optimizer 28 3.5 Communicating exogenous data to problem definition functions 28 3.5.1 Use of “global” data and variables 31 3.6 Masked (temporarily fixed) optimization parameters 32 3.7 Dealing with inadmissible results 33 3.8 Providing derivatives for functions 34 3.9 Derivative approximations when there are constraints 36 3.10 Scaling of parameters and function 36 3.11 Normal ending of computations 36 3.12 Termination tests – abnormal ending 37 3.13 Output to monitor progress of calculations 37 3.14 Output of the optimization results 38 3.15 Controls for the optimizer 38 3.16 Default control settings 39 3.17 Measuring performance 39 3.18 The optimization interface 39 References 40 4 One-parameter root-finding problems 41 4.1 Roots 41 4.2 Equations in one variable 42 4.3 Some examples 42 4.3.1 Exponentially speaking 42 4.3.2 A normal concern 44 4.3.3 Little Polly Nomial 46 4.3.4 A hypothequial question 49 4.4 Approaches to solving 1D root-finding problems 51 4.5 What can go wrong? 52 4.6 Being a smart user of root-finding programs 54 4.7 Conclusions and extensions 54 References 55 5 One-parameter minimization problems 56 5.1 The optimize() function 56 5.2 Using a root-finder 57 5.3 But where is the minimum? 58 5.4 Ideas for 1D minimizers 59 5.5 The line-search subproblem 61 References 62 6 Nonlinear least squares 63 6.1 nls() from package stats 63 6.1.1 A simple example 63 6.1.2 Regression versus least squares 65 6.2 A more difficult case 65 6.3 The structure of the nls() solution 72 6.4 Concerns with nls() 73 6.4.1 Small residuals 74 6.4.2 Robustness – “singular gradient” woes 75 6.4.3 Bounds with nls() 77 6.5 Some ancillary tools for nonlinear least squares 79 6.5.1 Starting values and self-starting problems 79 6.5.2 Converting model expressions to sum-of-squares functions 80 6.5.3 Help for nonlinear regression 80 6.6 Minimizing Rfunctions that compute sums of squares 81 6.7 Choosing an approach 82 6.8 Separable sums of squares problems 86 6.9 Strategies for nonlinear least squares 93 References 93 7 Nonlinear equations 95 7.1 Packages and methods for nonlinear equations 95 7.1.1 BB 96 7.1.2 nleqslv 96 7.1.3 Using nonlinear least squares 96 7.1.4 Using function minimization methods 96 7.2 A simple example to compare approaches 97 7.3 A statistical example 103 References 106 8 Function minimization tools in the base R system 108 8.1 optim() 108 8.2 nlm() 110 8.3 nlminb() 111 8.4 Using the base optimization tools 112 References 114 9 Add-in function minimization packages for R 115 9.1 Package optimx 115 9.1.1 Optimizers in optimx 116 9.1.2 Example use of optimx() 117 9.2 Some other function minimization packages 118 9.2.1 nloptr and nloptwrap 118 9.2.2 trust and trustOptim 119 9.3 Should we replace optim() routines? 121 References 122 10 Calculating and using derivatives 123 10.1 Why and how 123 10.2 Analytic derivatives – by hand 124 10.3 Analytic derivatives – tools 125 10.4 Examples of use of R tools for differentiation 125 10.5 Simple numerical derivatives 127 10.6 Improved numerical derivative approximations 128 10.6.1 The Richardson extrapolation 128 10.6.2 Complex-step derivative approximations 128 10.7 Strategy and tactics for derivatives 129 References 131 11 Bounds constraints 132 11.1 Single bound: use of a logarithmic transformation 132 11.2 Interval bounds: Use of a hyperbolic transformation 133 11.2.1 Example of the tanh transformation 134 11.2.2 A fly in the ointment 134 11.3 Setting the objective large when bounds are violated 135 11.4 An active set approach 136 11.5 Checking bounds 138 11.6 The importance of using bounds intelligently 138 11.6.1 Difficulties in applying bounds constraints 139 11.7 Post-solution information for bounded problems 139 Appendix 11.A Function transfinite 141 References 142 12 Using masks 143 12.1 An example 143 12.2 Specifying the objective 143 12.3 Masks for nonlinear least squares 147 12.4 Other approaches to masks 148 References 148 13 Handling general constraints 149 13.1 Equality constraints 149 13.1.1 Parameter elimination 151 13.1.2 Which parameter to eliminate? 153 13.1.3 Scaling and centering? 154 13.1.4 Nonlinear programming packages 154 13.1.5 Sequential application of an increasing penalty 156 13.2 Sumscale problems 158 13.2.1 Using a projection 162 13.3 Inequality constraints 163 13.4 A perspective on penalty function ideas 167 13.5 Assessment 167 References 168 14 Applications of mathematical programming 169 14.1 Statistical applications of math programming 169 14.2 R packages for math programming 170 14.3 Example problem: L1 regression 171 14.4 Example problem: minimax regression 177 14.5 Nonlinear quantile regression 179 14.6 Polynomial approximation 180 References 183 15 Global optimization and stochastic methods 185 15.1 Panorama of methods 185 15.2 R packages for global and stochastic optimization 186 15.3 An example problem 187 15.3.1 Method SANN from optim() 187 15.3.2 Package GenSA 188 15.3.3 Packages DEoptim and RcppDE 189 15.3.4 Package smco 191 15.3.5 Package soma 192 15.3.6 Package Rmalschains 193 15.3.7 Package rgenoud 193 15.3.8 Package GA 194 15.3.9 Package gaoptim 195 15.4 Multiple starting values 196 References 202 16 Scaling and reparameterization 203 16.1 Why scale or reparameterize? 203 16.2 Formalities of scaling and reparameterization 204 16.3 Hobbs’ weed infestation example 205 16.4 The KKT conditions and scaling 210 16.5 Reparameterization of the weeds problem 214 16.6 Scale change across the parameter space 214 16.7 Robustness of methods to starting points 215 16.7.1 Robustness of optimization techniques 218 16.7.2 Robustness of nonlinear least squares methods 220 16.8 Strategies for scaling 222 References 223 17 Finding the right solution 224 17.1 Particular requirements 224 17.1.1 A few integer parameters 225 17.2 Starting values for iterative methods 225 17.3 KKT conditions 226 17.3.1 Unconstrained problems 226 17.3.2 Constrained problems 227 17.4 Search tests 228 References 229 18 Tuning and terminating methods 230 18.1 Timing and profiling 230 18.1.1 rbenchmark 231 18.1.2 microbenchmark 231 18.1.3 Calibrating our timings 232 18.2 Profiling 234 18.2.1 Trying possible improvements 235 18.3 More speedups of R computations 238 18.3.1 Byte-code compiled functions 238 18.3.2 Avoiding loops 238 18.3.3 Package upgrades - an example 239 18.3.4 Specializing codes 241 18.4 External language compiled functions 242 18.4.1 Building an R function using Fortran 244 18.4.2 Summary of Rayleigh quotient timings 246 18.5 Deciding when we are finished 247 18.5.1 Tests for things gone wrong 248 References 249 19 Linking R to external optimization tools 250 19.1 Mechanisms to link R to external software 251 19.1.1 R functions to call external (sub)programs 251 19.1.2 File and system call methods 251 19.1.3 Thin client methods 252 19.2 Prepackaged links to external optimization tools 252 19.2.1 NEOS 252 19.2.2 Automatic Differentiation Model Builder (ADMB) 252 19.2.3 NLopt 253 19.2.4 BUGS and related tools 253 19.3 Strategy for using external tools 253 References 254 20 Differential equation models 255 20.1 The model 255 20.2 Background 256 20.3 The likelihood function 258 20.4 A first try at minimization 258 20.5 Attempts with optimx 259 20.6 Using nonlinear least squares 260 20.7 Commentary 261 Reference 262 21 Miscellaneous nonlinear estimation tools for R 263 21.1 Maximum likelihood 263 21.2 Generalized nonlinear models 266 21.3 Systems of equations 268 21.4 Additional nonlinear least squares tools 268 21.5 Nonnegative least squares 270 21.6 Noisy objective functions 273 21.7 Moving forward 274 References 275 Appendix A R packages used in examples 276 Index 279

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Book SynopsisTable of ContentsForeword xi About the Companion Website xiii 1 Data Cleaning 1 1.1 The Statistical Value Chain 1 1.1.1 Raw Data 2 1.1.2 Input Data 2 1.1.3 Valid Data 3 1.1.4 Statistics 3 1.1.5 Output 3 1.2 Notation and Conventions Used in this Book 3 2 A Brief Introduction to R 5 2.1 R on the Command Line 5 2.1.1 Getting Help and Learning R 6 2.2 Vectors 7 2.2.1 Computing with Vectors 9 2.2.2 Arrays and Matrices 10 2.3 Data Frames 11 2.3.1 The Formula-Data Interface 12 2.3.2 Selecting Rows and Columns; Boolean Operators 12 2.3.3 Selection with Indices 13 2.3.4 Data Frame Manipulation:The dplyr Package 14 2.4 Special Values 15 2.4.1 Missing Values 17 2.5 Getting Data into and out of R 18 2.5.1 File Paths in R 19 2.5.2 Formats Provided by Packages 20 2.5.3 Reading Data from a Database 20 2.5.4 Working with Data External to R 21 2.6 Functions 21 2.6.1 Using Functions 22 2.6.2 Writing Functions 22 2.7 Packages Used in this Book 23 3 Technical Representation of Data 27 3.1 Numeric Data 28 3.1.1 Integers 28 3.1.2 Integers in R 30 3.1.3 Real Numbers 31 3.1.4 Double Precision Numbers 31 3.1.5 The Concept of Machine Precision 33 3.1.6 Consequences ofWorking with Floating Point Numbers 34 3.1.7 Dealing with the Consequences 35 3.1.8 Numeric Data in R 37 3.2 Text Data 38 3.2.1 Terminology and Encodings 38 3.2.2 Unicode 39 3.2.3 Some Popular Encodings 40 3.2.4 Textual Data in R: Objects of Class Character 43 3.2.5 Encoding in R 44 3.2.6 Reading andWriting of Data with Non-Local Encoding 46 3.2.7 Detecting Encoding 48 3.2.8 Collation and Sorting 49 3.3 Times and Dates 50 3.3.1 AIT, UTC, and POSIX Seconds Since the Epcoch 50 3.3.2 Time and Date Notation 52 3.3.3 Time and Date Storage in R 54 3.3.4 Time and Date Conversion in R 55 3.3.5 Leap Days, Time Zones, and Daylight Saving Times 57 3.4 Notes on Locale Settings 58 4 Data Structure 61 4.1 Introduction 61 4.2 Tabular Data 61 4.2.1 data.frame 61 4.2.2 Databases 62 4.2.3 dplyr 64 4.3 Matrix Data 65 4.4 Time Series 66 4.5 Graph Data 68 4.6 Web Data 69 4.6.1 Web Scraping 69 4.6.2 Web API 70 4.7 Other Data 72 4.8 Tidying Tabular Data 72 4.8.1 Variable Per Column 74 4.8.2 Single Observation Stored in Multiple Tables 75 5 Cleaning Text Data 77 5.1 Character Normalization 78 5.1.1 Encoding Conversion and Unicode Normalization 78 5.1.2 Character Conversion and Transliteration 80 5.2 Pattern Matching with Regular Expressions 81 5.2.1 Basic Regular Expressions 82 5.2.2 Practical Regular Expressions 85 5.2.3 Generating Regular Expressions in R 92 5.3 Common String Processing Tasks in R 93 5.4 Approximate Text Matching 98 5.4.1 String Metrics 100 5.4.2 String Metrics and Approximate Text Matching in R 109 6 Data Validation 119 6.1 Introduction 119 6.2 A First Look at the validate Package 120 6.2.1 Quick Checks with check_that 120 6.2.2 The BasicWorkflow: validator and confront 122 6.2.3 A Little Background on validate and DSLs 124 6.3 Defining Data Validation 125 6.3.1 Formal Definition of Data Validation 126 6.3.2 Operations on Validation Functions 128 6.3.3 Validation and Missing Values 130 6.3.4 Structure of Validation Functions 131 6.3.5 Demarcating Validation Rules in validate 132 6.4 A Formal Typology of Data Validation Functions 134 6.4.1 A Closer Look at Measurement 134 6.4.2 Classification of Validation Rules 135 6.5 Validating Data with the validate Package 137 6.5.1 Validation Rules in the Console and the validator Object 137 6.5.2 Validating in the Pipeline 139 6.5.3 Raising Errors orWarnings 140 6.5.4 Tolerance for Testing Linear Equalities 140 6.5.5 Setting and Resetting Options 141 6.5.6 Importing and Exporting Validation Rules from and to File 142 6.5.7 Checking Variable Types and Metadata 145 6.5.8 Checking Value Ranges and Code Lists 146 6.5.9 Checking In-Record Consistency Rules 146 6.5.10 Checking Cross-Record Validation Rules 148 6.5.11 Checking Functional Dependencies 149 6.5.12 Cross-Dataset Validation 150 6.5.13 Macros, Variable Groups, Keys 152 6.5.14 Analyzing Output: validation Objects 152 6.5.15 Output Dimensionality and Output Selection 155 7 Localizing Errors in Data Records 157 7.1 Error Localization 157 7.2 Error Localization with R 160 7.2.1 The Errorlocate Package 160 7.3 Error Localization as MIP-Problem 163 7.3.1 Error Localization and Mixed-Integer Programming 163 7.3.2 Linear Restrictions 164 7.3.3 Categorical Restrictions 165 7.3.4 Mixed-Type Restrictions 167 7.4 Numerical Stability Issues 170 7.4.1 A Short Overview of MIP Solving 170 7.4.2 Scaling Numerical Records 172 7.4.3 Setting NumericalThreshold Values 173 7.5 Practical Issues 174 7.5.1 Setting ReliabilityWeights 174 7.5.2 Simplifying Conditional Validation Rules 176 7.6 Conclusion 180 8 Rule Set Maintenance and Simplification 183 8.1 Quality of Validation Rules 183 8.1.1 Completeness 183 8.1.2 Superfluous Rules and Infeasibility 184 8.2 Rules in the Language of Logic 184 8.2.1 Using Logic to Rewrite Rules 185 8.3 Rule Set Issues 186 8.3.1 Infeasible Rule Set 186 8.3.2 Fixed Value 187 8.3.3 Redundant Rule 188 8.3.4 Nonrelaxing Clause 189 8.3.5 Nonconstraining Clause 189 8.4 Detection and Simplification Procedure 190 8.4.1 Mixed-Integer Programming 190 8.4.2 Detecting Feasibility 191 8.4.3 Finding Rules Causing Infeasibility 191 8.4.4 Detecting Conflicting Rules 191 8.4.5 Detect Partial Infeasibility 192 8.4.6 Detect Fixed Values 192 8.4.7 Detect Nonrelaxing Clauses 192 8.4.8 Detecting Nonconstraining Clauses 193 8.4.9 Detecting Redundant Rules 193 8.5 Conclusion 194 9 Methods Based on Models for Domain Knowledge 195 9.1 Correction with Data Modifying Rules 195 9.1.1 Modifying Functions 196 9.1.2 A Class of Modifying Functions on Numerical Data 201 9.2 Rule-Based Correction with dcmodify 205 9.2.1 Reading Rules from File 206 9.2.2 Modifying Rule Syntax 207 9.2.3 Missing Values 208 9.2.4 Sequential and Sequence-Independent Execution 208 9.2.5 Options Settings Management 209 9.3 Deductive Correction 209 9.3.1 Correcting Typing Errors in Numeric Data 209 9.3.2 Deductive Imputation Using Linear Restrictions 213 10 Imputation and Adjustment 219 10.1 Missing Data 219 10.1.1 Missing Data Mechanisms 219 10.1.2 Visualizing and Testing for Patterns in Missing Data Using R 220 10.2 Model-Based Imputation 224 10.3 Model-Based Imputation in R 226 10.3.1 Specifying ImputationMethods with simputation 226 10.3.2 Linear Regression-Based Imputation 227 10.3.3 M-Estimation 230 10.3.4 Lasso, Ridge, and Elasticnet Regression 231 10.3.5 Classification and Regression Trees 232 10.3.6 Random Forest 235 10.4 Donor Imputation with R 236 10.4.1 Random and Sequential Hot Deck Imputation 237 10.4.2 k Nearest Neighbors and Predictive Mean Matching 238 10.5 Other Methods in the simputation Package 239 10.6 Imputation Based on the EM Algorithm 240 10.6.1 The EM Algorithm 241 10.6.2 EM Imputation Assuming the Multivariate Normal Distribution 243 10.7 Sampling Variance under Imputation 244 10.8 Multiple Imputations 246 10.8.1 Multiple Imputation Based on the EM Algorithm 248 10.8.2 The Amelia Package 249 10.8.3 Multivariate Imputation with Chained Equations (Mice) 252 10.8.4 Imputation with the mice Package 254 10.9 Analytic Approaches to Estimate Variance of Imputation 256 10.9.1 Imputation as Part of the Estimator 256 10.10 Choosing an ImputationMethod 257 10.11 Constraint Value Adjustment 259 10.11.1 Formal Description 259 10.11.2 Application to Imputed Data 262 10.11.3 Adjusting Imputed Values with the rspa Package 263 11 Example: A Small Data-Cleaning System 265 11.1 Setup 266 11.1.1 DeterministicMethods 266 11.1.2 Error Localization 269 11.1.3 Imputation 269 11.1.4 Adjusting Imputed Data 271 11.2 Monitoring Changes in Data 273 11.2.1 Data Diff (Daff) 274 11.2.2 Summarizing Cell Changes 275 11.2.3 Summarizing Changes in Conformance to Validation Rules 277 11.2.4 Track Changes in Data Automatically with lumberjack 278 11.3 Integration and Automation 282 11.3.1 Using RScript 283 11.3.2 The docopt Package 283 11.3.3 Automated Data Cleaning 285 References 287 Index 297

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Book SynopsisTransform your organization''s data into actionable insights with Tableau Tableau is designed specifically to provide fast and easy visual analytics. The intuitive drag-and-drop interface helps you create interactive reports, dashboards, and visualizations, all without any special or advanced training. This all new edition of Tableau Your Data! is your Tableau companion, helping you get the most out of this invaluable business toolset.Tableau Your Data! shows you how to build dynamic, best of breed visualizations using the Tableau Software toolset. This comprehensive guide covers the core feature set for data analytics, and provides clear step-by-step guidance toward best practices and advanced techniques that go way beyond the user manual. You''ll learn how Tableau is different from traditional business information analysis tools, and how to navigate your way around the Tableau 9.0 desktop before delving into functions and calculations, as well as sTable of ContentsIntroduction xxv Part I Desktop 1 1 Creating Visual Analytics with Tableau Desktop 3 The Shortcomings of Traditional Information Analysis 4 The Business Case for Visual Analysis 5 Three Kinds of Data That Exist in Every Entity 5 How Visual Analytics Improves Decision Making 6 Turning Data into Information with Visual Analytics 8 Analysis as a Creative Process 8 Tableau’s Desktop Tools 9 Tableau Desktop Personal Edition 9 Professional Edition 9 Tableau File Types 9 Tableau Reader 11 Tableau Online Help 11 Introducing the Tableau Desktop Workspace 11 New Workspace Design 11 Using the Start Page Controls Effectively 12 The Start Page 12 The Tableau Desktop Workspace 17 Summary 41 2 Connecting to Your Data 43 What You Will Learn in This Chapter 43 How to Connect to Your Data 44 Connecting to Desktop Sources 45 Understanding the Data Source Page 47 What Are Generated Values? 57 Knowing When to Use a Direct Connection or a Data Extract 61 Using Tableau’s File Types Effectively 63 Dealing with Data Shaping and Data Quality 65 The Data Interpreter 68 3 Building Your First Visualization 93 Fast and Easy Analysis via Show Me 93 New Features 94 How Show Me Works 94 The Analytics Pane 103 Sorting Data in Tableau 118 Enhancing View with Filters, Sets, Groups, and Hierarchies 121 How Tableau Uses Date Fields 143 4 Creating Calculations to Enhance Data 155 What Is Aggregation? 156 Dimension versus Attribute 157 What Are Calculated Fields and Table Calculations? 159 How Do Calculated Fields Work? 159 Creating Calculated Fields with the Calculation Editor 160 Performing Ad Hoc Calculations 161 How Do Table Calculations Work? 161 A Word on Calculations and Cubes 162 Using the Calculation Editor to Build Calculated Fields 163 Ad Hoc Calculated Fields 164 Building Formulas Using Table Calculations 166 Adding Flexibility to Calculations with Parameters 177 Why You Should Learn Level of Detail Expressions 183 5 Using Maps to Improve Insight 191 New Map Features 192 Creating a Standard Map View 192 How Tableau Geocodes Your Data 195 Searching for Items in Maps 197 Typical Map Errors and How to Deal with Them 199 Plotting Your Own Locations on a Map 200 Replacing Tableau’s Standard Maps 205 Using Custom Background Images to Plot Spatial Data 211 Notes 219 6 Developing an Ad Hoc Analysis Environment 221 Data Discovery as a Creative Process 221 Preparing Your Team for Success 222 Qualities of a Good Data Analyst 223 Doing Effective Discovery Work 224 What IT Can Do to Help 224 Spreading Discovery to Information Consumers 225 Generating New Data with Forecasts 225 Providing Self-Service Ad Hoc Analysis with Parameters 231 What Are Parameters? 231 How Can Parameters Be Used? 231 Basic Parameter Controls 232 Advanced Parameter Controls 236 Editing Views in Tableau Server 239 7 Tips, Tricks, and Timesavers 243 Saving Time and Improving Formatting 243 Double-Click Fields to Build Faster 243 Reduce Clicks Using the Right Mouse Button Drag 245 Quick Copy Fields with Control-Drag 246 Replace Fields by Dropping the New Field on Top 246 Right-Click to Edit or Format Anything 247 Editing or Removing Titles from Axis Headings 247 Quicken Your Presentation Page Views 248 A Faster Way to Access Field Menu Options 250 Zooming the Formula Dialog Box 250 Drag a Field into the Formula Dialog box 250 Swap Data in Pane and Reference Line Fields 251 Improving Appearance to Convey Meaning More Precisely 251 Changing the Appearance of Dates 251 Formatting Tooltip Content 252 Change the Order of Color Expressed in Charts 252 Exposing a Header in a One-Column Text Table 253 Unpacking a Packaged Workbook File 255 Make a Parameterized Axis Label 255 Using Continuous Quick Filters for Ranges of Values 256 Create Your Own Custom Date Hierarchy 256 Concatenating to Make Custom Fields 258 Using Legends to Build Highlight Actions 258 Formatting Null Value Results 260 When to Use Floating Objects in Dashboards 264 Combined Axis Shading in a Scatter Plot 266 Creating Folders to Hold Fields 268 Customizing Shapes, Colors, Fonts, and Images 269 Customizing Shapes 269 Customizing Colors 271 Customizing Fonts 272 Customizing Images in Dashboards 273 Advanced Chart Types 274 Bar-in-Bar Chart 274 Pareto Charts 275 Sparklines 280 Bullet Graphs 281 8 Bringing It All Together with Dashboards 285 How Dashboards Facilitate Analysis and Understanding 285 How Tableau Improves the Dashboard-Building Process 286 The Wrong Way to Build a Dashboard 287 The Right Way to Build a Dashboard 289 Best Practices for Dashboard Building 290 Size the Dashboard to Fit the Worst-Case Available Space 291 Employ Four-Pane Dashboard Designs 291 Use Actions to Filter Instead of Quick Filters 293 Build Cascading Dashboard Designs to Improve Load Speeds 293 Limit the Use of Color to One Primary Color Scheme 294 Use Small Instructions Near the Work to Make Navigation Obvious 295 Filter Information Presented in Crosstabs to Provide Relevant Details-on-Demand 296 Remove All Non-Data-Ink 298 Avoid One-Size-Fits-All Dashboards 298 Work to Achieve Dashboard Load Times of Less Than Ten Seconds 299 Building Your First Advanced Dashboard 299 Introducing the Dashboard Worksheet 299 Position the Worksheet Objects in the Dashboard Workspace 304 Using Layout Containers to Position Objects 308 Positioning the Select Year Text Table and Legends 311 Inserting and Moving Text Objects 312 Positioning and Fitting the Dashboard Objects 315 Ensure That Each Worksheet Object Fits Its Entire View 316 Create More Descriptive Titles for Each Data Pane 317 Improving the Bullet Graph and Sparkline Charts 318 Improving the Text Tables and Scatter Plot 326 Using Actions to Create Advanced Dashboard Navigation 328 Using the Select Year Text Table to Filter the Main Dashboard 329 Adding a Column Heading to Select Year 331 Adding Dynamic Title Content 332 Auto-Generating Highlight Actions from Legends 333 Understanding the Action Dialog Box 336 Embedding a Live Website in a Dashboard 340 Assemble Dashboard 2 345 Adding Details on Demand with Tooltips 354 Enhancing Tooltips and Titles 356 Adding a Read Me Dashboard 358 Bonus: Adding a Floating Dashboard Object 359 Finishing the Titles in the Main Dashboard 363 Sharing Your Dashboard with Tableau Reader 364 Security Considerations for Publishing via Tableau Reader 365 Using the Tableau Performance Recorder to Improve Load Speed 366 Sharing Dashboards with Tableau Online or Tableau Server 367 9 Designing for Mobile 369 The Physics of Mobile Consumption 370 Security Considerations for Mobile Consumption 370 Offline Access 371 Typical Mobile Usage Patterns 373 Just-In-Time Use 373 Mobile Design Implications 374 Design Best Practices for Mobile Consumption 374 Design Implications Related to Screen Resolution 375 Best Practices for Mobile Design 375 Design for a Specific Orientation 375 Consider the Limits of Finger Navigation 375 Reduce the Number of Worksheets Being Displayed 378 A Tablet Dashboard Example 378 Mobile Authoring and Editing 382 A Note on Project Elastic 383 10 Conveying Your Findings with Stories 385 Turning Analysis into Insight 385 Building a Story 386 The Story Workspace 387 A Story Example 389 Formatting Story Points 390 Sharing Your Story Point Deck 391 Part II Server 393 11 Installing Tableau Server 395 What’s New in Version 9? 396 Reasons to Deploy Tableau Server 397 Data Governance 398 Efficiency 398 Flexibility 399 Licensing Options for Tableau Server and Tableau Online 399 Determining Your Hardware and Software Needs 399 New Feature: Persistent Query Cache 401 Determining What Kind of Server License to Purchase 401 Tableau Server’s Architecture 402 Sizing the Server Hardware 403 A Scale-Up Scenario 404 A Scale-Out Scenario 404 Environmental Factors That Can Affect Performance 405 Network Performance 405 Browser 405 Resource Contention 405 Configuring Tableau Server for the First Time 405 General Setup Menu Tab 406 General: Run as User, User Authentication, and Active Directory 407 General: Gateway Port Number 408 General: Open Port in Windows Firewall 408 General: Include Sample Data and Users 408 Data Connection Tab 409 Alerts and Subscriptions 410 Server Processes 411 Security Options 412 External Secure Sockets Layer 414 SAML—Security Assertion Markup Language 415 Kerberos—A Ticket-Based Security Protocol 416 Managing Ownership Through Hierarchy 417 Workbooks and Views 417 User 418 Project 418 Group 418 Site 418 Permissions 419 Permissions for Web Edit, Save, and Download 420 Providing Data Security with User Filters 421 Applying a User Filter to a Data Source 424 Creating a Hybrid Filter from the Data Source 425 What Is the Data Server? 427 When and How to Deploy Server on Multiple Physical Boxes 428 Deploying Tableau Server in High Availability Environments 429 Three-Node Cluster 429 Four-Node Cluster 430 Leveraging Existing Security with Trusted Authentication 432 Deploying Tableau Server in Multi-national Environments 434 Tableau Server Performance Recorder 436 Show Events Filter 438 Timeline Gantt Chart 439 Events Sorted by Time 439 Query Text 439 Performance-Tuning Tactics 439 Query Execution 439 Geocoding 439 Connecting to the Data Source 440 Layout Computations 440 Generating Extract 440 Blending Data 441 Server Rendering 441 Managing Tableau Server in the Cloud 441 What Does It Mean to Be in the Cloud? 441 Tableau’s Cloud-Based Versions of Server 442 Putting Tableau Server in the Cloud 443 Monitoring Activity on Tableau Server 443 Status Section 445 Analysis Section 445 Log Files Section 445 Rebuilt Search Index Section 446 Editing Server Settings and Monitoring Licensing 446 Server Settings General Page 446 Server Setting License Page 447 Partner Add-On Toolkits 448 12 Managing Tableau Server 449 Managing Published Dashboards in Tableau Server 449 Project 451 Name 452 Tags 452 Views to Share 452 Options 452 Edit 453 Navigating Tableau Server 454 Organizing Reports for Consumption 457 Adding Tags to Workbooks 458 Creating a Favorite 459 Options for Securing Reports 461 The Application Layer 461 Defining Custom Roles 462 A Permission-Setting Example 464 Improve Efficiency with the Data Server 469 Publishing a Data Source 469 Consuming Information in Tableau Server 474 Finding Information 475 Authoring and Editing Reports via Server 480 What Is Required to Author Reports on the Web? 480 Server Design and Usage Considerations Related to Web and Tablet Authoring 481 Differences Between Desktop and Web or Tablet Authoring 482 Saving and Exporting via the Web-Tablet Environment 488 Export 488 Save and Save As 489 Recommendations for Implementing Web/Tablet Authoring 489 Sharing Connections, Data Models, and Data Extracts 490 Offering a Common Data Library 490 Sharing Data Models 490 Embedding Tableau Reports Securely on the Web 491 When to Embed a Dashboard 491 When Your Reports Are a Piece of a Larger SaaS Offering 491 Providing a More Robust Environment 492 How to Embed a Dashboard 492 Further Control Using Passed Parameters 494 Tips and Tricks for Embedding Dashboards 494 Using Trusted Ticket Authentication as an Alternative Single Sign-On Method 495 Using Subscriptions to Deliver Reports via E‑mail 496 Creating Subscription Schedules 496 13 Automating Tableau Server 501 Tableau Server’s APIs 501 What Do Tabcmd and Tabadmin Do? 502 Installing the Command-Line Tools 502 Setting the Windows Path 505 What Kind of Tasks Can Tabcmd Do? 506 Learning to Leverage Tabcmd 507 Manually Entering and Running a Script in Tabcmd 508 Running Tabcmd Scripts via Batch Files 509 The Steps Required to Create Batch Processing Scripts 509 Using Windows Scheduler to Fully Automate Scripts 511 Common Use Cases for Tabcmd 513 Automating Extracts with the Extract API 515 Data Extract API 515 Using the Extract API with Python 517 Data Extract Command-Line Utility 520 REST API 521 Initial Transactions 521 Part III Case Studies 527 14 Ensuring a Successful Tableau Deployment 529 Deploying Tableau—Lessons Learned 529 Effective Use of Consultants 529 Your Team’s Current Knowledge 530 The Data Landscape 530 The Tableau User Group at Cigna 531 Taking Care of Vizness 531 Resourcing 532 Cadence 532 Format 533 Topics 533 Effectiveness and Attendance 534 Tracking Participation 535 Success 535 Part IV Appendixes 537 A Tableau’s Product Ecosystem 539 B S upported Data Source Connections 543 C Keyboard Shortcuts 547 D Recommended Hardware Configurations 551 E Understanding Tableau Functions 555 F Companion Website 657 Glossary 659 Index 673

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Book SynopsisFeatures a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians.Table of ContentsList of Symbols xv Preface xvii About the Companion Website xix 1 Introduction 1 1.1 Samples Versus Populations 3 1.2 Comments on Software 4 1.3 R Basics 5 1.3.1 Entering Data 6 1.3.2 Arithmetic Operations 10 1.3.3 Storage Types and Modes 12 1.3.4 Identifying and Analyzing Special Cases 17 1.4 R Packages 20 1.5 Access to Data Used in this Book 22 1.6 Accessing More Detailed Answers to the Exercises 23 1.7 Exercises 23 2 Numerical Summaries of Data 25 2.1 Summation Notation 26 2.2 Measures of Location 29 2.2.1 The Sample Mean 29 2.2.2 The Median 30 2.2.3 Sample Mean versus Sample Median 33 2.2.4 Trimmed Mean 34 2.2.5 R function mean, tmean, and median 35 2.3 Quartiles 36 2.3.1 R function idealf and summary 37 2.4 Measures of Variation 37 2.4.1 The Range 38 2.4.2 R function Range 38 2.4.3 Deviation Scores, Variance, and Standard Deviation 38 2.4.4 R Functions var and sd 40 2.4.5 The Interquartile Range 41 2.4.6 MAD and the Winsorized Variance 41 2.4.7 R Functions winvar, winsd, idealfIQR, and mad 44 2.5 Detecting Outliers 44 2.5.1 A Classic Outlier Detection Method 45 2.5.2 The Boxplot Rule 46 2.5.3 The MAD–Median Rule 47 2.5.4 R Functions outms, outbox, and out 47 2.6 Skipped Measures of Location 48 2.6.1 R Function MOM 49 2.7 Summary 49 2.8 Exercises 50 3 Plots Plus More Basics on Summarizing Data 53 3.1 Plotting Relative Frequencies 53 3.1.1 R Functions table, plot, splot, barplot, and cumsum 54 3.1.2 Computing the Mean and Variance Based on the Relative Frequencies 56 3.1.3 Some Features of the Mean and Variance 57 3.2 Histograms and Kernel Density Estimators 57 3.2.1 R Function hist 58 3.2.2 What Do Histograms Tell Us? 59 3.2.3 Populations, Samples, and Potential Concerns about Histograms 61 3.2.4 Kernel Density Estimators 64 3.2.5 R Functions Density and Akerd 64 3.3 Boxplots and Stem-and-Leaf Displays 65 3.3.1 R Function stem 67 3.3.2 Boxplot 67 3.3.3 R Function boxplot 68 3.4 Summary 68 3.5 Exercises 69 4 Probability and Related Concepts 71 4.1 The Meaning of Probability 71 4.2 Probability Functions 72 4.3 Expected Values, Population Mean and Variance 74 4.3.1 Population Variance 76 4.4 Conditional Probability and Independence 77 4.4.1 Independence and Dependence 78 4.5 The Binomial Probability Function 80 4.5.1 R Functions dbinom and pbinom 85 4.6 The Normal Distribution 85 4.6.1 Some Remarks about the Normal Distribution 88 4.6.2 The Standard Normal Distribution 89 4.6.3 Computing Probabilities for Any Normal Distribution 92 4.6.4 R Functions pnorm and qnorm 94 4.7 Nonnormality and The Population Variance 94 4.7.1 Skewed Distributions 97 4.7.2 Comments on Transforming Data 98 4.8 Summary 100 4.9 Exercises 101 5 Sampling Distributions 107 5.1 Sampling Distribution of ̂p, the Proportion of Successes 108 5.2 Sampling Distribution of the Mean Under Normality 111 5.2.1 Determining Probabilities Associated with the Sample Mean 113 5.2.2 But Typically 𝜎 Is Not Known. Now What? 116 5.3 Nonnormality and the Sampling Distribution of the Sample Mean 116 5.3.1 Approximating the Binomial Distribution 117 5.3.2 Approximating the Sampling Distribution of the Sample Mean: The General Case 119 5.4 Sampling Distribution of the Median and 20% Trimmed Mean 123 5.4.1 Estimating the Standard Error of the Median 126 5.4.2 R Function msmedse 127 5.4.3 Approximating the Sampling Distribution of the Sample Median 128 5.4.4 Estimating the Standard Error of a Trimmed Mean 129 5.4.5 R Function trimse 130 5.4.6 Estimating the Standard Error When Outliers Are Discarded: A Technically Unsound Approach 130 5.5 The Mean Versus the Median and 20% Trimmed Mean 131 5.6 Summary 135 5.7 Exercises 136 6 Confidence Intervals 139 6.1 Confidence Interval for the Mean 139 6.1.1 Computing a Confidence Interval Given 𝜎2 140 6.2 Confidence Intervals for the Mean Using s (𝜎 Not Known) 145 6.2.1 R Function t.test 148 6.3 A Confidence Interval for The Population Trimmed Mean 149 6.3.1 R Function trimci 150 6.4 Confidence Intervals for The Population Median 151 6.4.1 R Function msmedci 152 6.4.2 Underscoring a Basic Strategy 152 6.4.3 A Distribution-Free Confidence Interval for the Median Even When There Are Tied Values 153 6.4.4 R Function sint 154 6.5 The Impact of Nonnormality on Confidence Intervals 155 6.5.1 Student’s T and Nonnormality 155 6.5.2 Nonnormality and the 20% Trimmed Mean 161 6.5.3 Nonnormality and the Median 162 6.6 Some Basic Bootstrap Methods 163 6.6.1 The Percentile Bootstrap Method 163 6.6.2 R Functions trimpb 164 6.6.3 Bootstrap-t 164 6.6.4 R Function trimcibt 166 6.7 Confidence Interval for The Probability of Success 167 6.7.1 Agresti–Coull Method 169 6.7.2 Blyth’s Method 169 6.7.3 Schilling–Doi Method 170 6.7.4 R Functions acbinomci and binomLCO 170 6.8 Summary 172 6.9 Exercises 173 7 Hypothesis Testing 179 7.1 Testing Hypotheses about the Mean, 𝜎 Known 179 7.1.1 Details for Three Types of Hypotheses 180 7.1.2 Testing for Exact Equality and Tukey’s Three-Decision Rule 183 7.1.3 p-Values 184 7.1.4 Interpreting p-Values 186 7.1.5 Confidence Intervals versus Hypothesis Testing 187 7.2 Power and Type II Errors 187 7.2.1 Power and p-Values 191 7.3 Testing Hypotheses about the mean, 𝜎 Not Known 191 7.3.1 R Function t.test 193 7.4 Student’s T and Nonnormality 193 7.4.1 Bootstrap-t 195 7.4.2 Transforming Data 196 7.5 Testing Hypotheses about Medians 196 7.5.1 R Function msmedci and sintv2 197 7.6 Testing Hypotheses Based on a Trimmed Mean 198 7.6.1 R Functions trimci, trimcipb, and trimcibt 198 7.7 Skipped Estimators 200 7.7.1 R Function momci 200 7.8 Summary 201 7.9 Exercises 202 8 Correlation and Regression 207 8.1 Regression Basics 207 8.1.1 Residuals and a Method for Estimating the Median of Y Given X 209 8.1.2 R function qreg and Qreg 211 8.2 Least Squares Regression 212 8.2.1 R Functions lsfit, lm, ols, plot, and abline 214 8.3 Dealing with Outliers 215 8.3.1 Outliers among the Independent Variable 215 8.3.2 Dealing with Outliers among the Dependent Variable 216 8.3.3 R Functions tsreg and tshdreg 218 8.3.4 Extrapolation Can Be Dangerous 219 8.4 Hypothesis Testing 219 8.4.1 Inferences about the Least Squares Slope and Intercept 220 8.4.2 R Functions lm, summary, and ols 223 8.4.3 Heteroscedcasticity: Some Practical Concerns and How to Address Them 225 8.4.4 R Function olshc4 226 8.4.5 Outliers among the Dependent Variable: A Cautionary Note 227 8.4.6 Inferences Based on the Theil–Sen Estimator 227 8.4.7 R Functions regci and regplot 227 8.5 Correlation 229 8.5.1 Pearson’s Correlation 229 8.5.2 Inferences about the Population Correlation, 𝜌 232 8.5.3 R Functions pcor and pcorhc4 234 8.6 Detecting Outliers When Dealing with Two or More Variables 235 8.6.1 R Functions out and outpro 236 8.7 Measures of Association: Dealing with Outliers 236 8.7.1 Kendall’s Tau 236 8.7.2 R Functions tau and tauci 239 8.7.3 Spearman’s Rho 240 8.7.4 R Functions spear and spearci 241 8.7.5 Winsorized and Skipped Correlations 242 8.7.6 R Functions scor, scorci, scorciMC, wincor, and wincorci 243 8.8 Multiple Regression 245 8.8.1 Least Squares Regression 245 8.8.2 Hypothesis Testing 246 8.8.3 R Function olstest 248 8.8.4 Inferences Based on a Robust Estimator 248 8.8.5 R Function regtest 249 8.9 Dealing with Curvature 249 8.9.1 R Function lplot and rplot 251 8.10 Summary 256 8.11 Exercises 257 9 Comparing Two Independent Groups 263 9.1 Comparing Means 264 9.1.1 The Two-Sample Student’s T Test 264 9.1.2 Violating Assumptions When Using Student’s T 266 9.1.3 Why Testing Assumptions Can Be Unsatisfactory 269 9.1.4 Interpreting Student’s T When It Rejects 270 9.1.5 Dealing with Unequal Variances: Welch’s Test 271 9.1.6 R Function t.test 273 9.1.7 Student’s T versus Welch’s Test 274 9.1.8 The Impact of Outliers When Comparing Means 275 9.2 Comparing Medians 276 9.2.1 A Method Based on the McKean–Schrader Estimator 276 9.2.2 A Percentile Bootstrap Method 277 9.2.3 R Functions msmed, medpb2, split, and fac2list 278 9.2.4 An Important Issue: The Choice of Method can Matter 279 9.3 Comparing Trimmed Means 280 9.3.1 R Functions yuen, yuenbt, and trimpb2 282 9.3.2 Skipped Measures of Location and Deleting Outliers 283 9.3.3 R Function pb2gen 283 9.4 Tukey’s Three-Decision Rule 283 9.5 Comparing Variances 284 9.5.1 R Function comvar2 285 9.6 Rank-Based (Nonparametric) Methods 285 9.6.1 Wilcoxon–Mann–Whitney Test 286 9.6.2 R Function wmw 289 9.6.3 Handling Heteroscedasticity 289 9.6.4 R Functions cid and cidv2 290 9.7 Measuring Effect Size 291 9.7.1 Cohen’s d 292 9.7.2 Concerns about Cohen’s d and How They Might Be Addressed 293 9.7.3 R Functions akp.effect, yuenv2, and med.effect 295 9.8 Plotting Data 296 9.8.1 R Functions ebarplot, ebarplot.med, g2plot, and boxplot 298 9.9 Comparing Quantiles 299 9.9.1 R Function qcomhd 300 9.10 Comparing Two Binomial Distributions 301 9.10.1 Improved Methods 302 9.10.2 R Functions twobinom and twobicipv 302 9.11 A Method for Discrete or Categorical Data 303 9.11.1 R Functions disc2com, binband, and splotg2 304 9.12 Comparing Regression Lines 305 9.12.1 Classic ANCOVA 307 9.12.2 R Function CLASSanc 307 9.12.3 Heteroscedastic Methods for Comparing the Slopes and Intercepts 309 9.12.4 R Functions olsJ2 and ols2ci 309 9.12.5 Dealing with Outliers among the Dependent Variable 311 9.12.6 R Functions reg2ci, ancGpar, and reg2plot 311 9.12.7 A Closer Look at Comparing Nonparallel Regression Lines 313 9.12.8 R Function ancJN 313 9.13 Summary 315 9.14 Exercises 316 10 Comparing More than Two Independent Groups 321 10.1 The ANOVA F Test 321 10.1.1 R Functions anova, anova1, aov, split, and fac2list 327 10.1.2 When Does the ANOVA F Test Perform Well? 329 10.2 Dealing with Unequal Variances: Welch’s Test 331 10.3 Comparing Groups Based on Medians 333 10.3.1 R Functions med1way and Qanova 333 10.4 Comparing Trimmed Means 334 10.4.1 R Functions t1way and t1waybt 335 10.5 Two-Way ANOVA 335 10.5.1 Interactions 338 10.5.2 R Functions anova and aov 341 10.5.3 Violating Assumptions 342 10.5.4 R Functions t2way and t2waybt 343 10.6 Rank-Based Methods 344 10.6.1 The Kruskal–Wallis Test 344 10.6.2 Method BDM 346 10.7 R Functions kruskal.test AND bdm 347 10.8 Summary 348 10.9 Exercises 349 11 Comparing Dependent Groups 353 11.1 The Paired T Test 354 11.1.1 When Does the Paired T Test Perform Well? 356 11.1.2 R Functions t.test and trimcibt 357 11.2 Comparing Trimmed Means and Medians 357 11.2.1 R Functions yuend, ydbt, and dmedpb 359 11.2.2 Measures of Effect Size 363 11.2.3 R Functions D.akp.effect and effectg 364 11.3 The SIGN Test 364 11.3.1 R Function signt 365 11.4 Wilcoxon Signed Rank Test 365 11.4.1 R Function wilcox.test 367 11.5 Comparing Variances 367 11.5.1 R Function comdvar 368 11.6 Dealing with More Than Two Dependent Groups 368 11.6.1 Comparing Means 369 11.6.2 R Function aov 369 11.6.3 Comparing Trimmed Means 370 11.6.4 R Function rmanova 371 11.6.5 Rank-Based Methods 371 11.6.6 R Functions friedman.test and bprm 373 11.7 Between-By-Within Designs 373 11.7.1 R Functions bwtrim and bw2list 373 11.8 Summary 375 11.9 Exercises 376 12 Multiple Comparisons 379 12.1 Classic Methods for Independent Groups 380 12.1.1 Fisher’s Least Significant Difference Method 380 12.1.2 R Function FisherLSD 382 12.2 The Tukey–Kramer Method 382 12.2.1 Some Important Properties of the Tukey–Kramer Method 384 12.2.2 R Functions TukeyHSD and T.HSD 385 12.3 Scheffé’s Method 386 12.3.1 R Function Scheffe 386 12.4 Methods That Allow Unequal Population Variances 387 12.4.1 Dunnett’s T3 Method and an Extension of Yuen’s Method for Comparing Trimmed Means 387 12.4.2 R Functions lincon, linconbt, and conCON 389 12.5 Anova Versus Multiple Comparison Procedures 391 12.6 Comparing Medians 391 12.6.1 R Functions msmed, medpb, and Qmcp 392 12.7 Two-Way Anova Designs 393 12.7.1 R Function mcp2atm 397 12.8 Methods For Dependent Groups 400 12.8.1 Bonferroni Method 400 12.8.2 Rom’s Method 401 12.8.3 Hochberg’s Method 403 12.8.4 R Functions rmmcp, dmedpb, and sintmcp 403 12.8.5 Controlling the False Discovery Rate 404 12.9 Summary 405 12.10 Exercises 406 13 Categorical Data 409 13.1 One-Way Contingency Tables 409 13.1.1 R Function chisq.test 413 13.1.2 Gaining Perspective: A Closer Look at the Chi-Squared Distribution 413 13.2 Two-Way Contingency Tables 414 13.2.1 McNemar’s Test 414 13.2.2 R Functions contab and mcnemar.test 417 13.2.3 Detecting Dependence 418 13.2.4 R Function chi.test.ind 422 13.2.5 Measures of Association 422 13.2.6 The Probability of Agreement 423 13.2.7 Odds and Odds Ratio 424 13.3 Logistic Regression 426 13.3.1 R Function logreg 428 13.3.2 A Confidence Interval for the Odds Ratio 429 13.3.3 R Function ODDSR.CI 429 13.3.4 Smoothers for Logistic Regression 429 13.3.5 R Functions rplot.bin and logSM 430 13.4 Summary 431 13.5 Exercises 432 AppendixA Solutions to Selected Exercises 435 Appendix B Tables 441 References 465 Index 473

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Book SynopsisStatistics with JMP: Hypothesis Tests, ANOVA and Regression Peter Goos, University of Leuven and University of Antwerp, Belgium David Meintrup, University of Applied Sciences Ingolstadt, Germany A first course on basic statistical methodology using JMP This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software. Key features: Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested. Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). Discusses the power of various statistical tests, along with examples in JMP to Trade Review"Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering." (Zentralblatt MATH 2016)Table of ContentsDedication iii Preface xiii Acknowledgements xvii Part One Estimators and tests 1 1 Estimating population parameters 3 2 Interval estimators 37 3 Hypothesis tests 71 Part Two One population 103 4 Hypothesis tests for a population mean, proportion or variance 105 5 Two hypothesis tests for the median of a population 149 6 Hypothesis tests for the distribution of a population 175 Part Three Two populations 7 Independent versus paired samples 213 8 Hypothesis tests for means, proportions and variances of two independent samples 219 9 A nonparametric hypothesis test for the medians of two independent samples 263 10 Hypothesis tests for the population mean of two paired samples 285 11 Two nonparametric hypothesis tests for paired samples 305 Part Four More than two populations 325 12 Hypothesis tests for more than two population means: one-way analysis of variance 327 13 Nonparametric alternatives to an analysis of variance 375 14 Hypothesis tests for more than two population variances 401 Part Five More useful tests and procedures 417 15 Design of experiments and data collection 419 16 Testing equivalence 427 17 Estimation and testing of correlation and association 445 18 An introduction to regression modeling 481 19 Simple linear regression 493 A Binomial distribution 589 B Standard normal distribution 593 C X2-distribution 595 D Student’s t-distribution 597 E Wilcoxon signed-rank test 599 F Critical values for the Shapiro-Wilk test 605 G Fisher’s F-distribution 607 H Wilcoxon rank-sum test 615 I Studentized range or Q-distribution 625 J Two-sided Dunnett test 629 K One-sided Dunnett test 633 L Kruskal-Wallis-Test 637 M Rank correlation test 641 Index 643

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Book SynopsisA must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk coTable of ContentsPreface to the Second Edition xi Preface xiii Abbreviations xv About the Companion Website xix PART I MOTIVATION 1 1 Introduction 3 Reference 5 2 A brief course in R 6 2.1 Origin and development 6 2.2 Getting help 7 2.3 Working with R 10 2.4 Classes, methods, and functions 12 2.5 The accompanying package FRAPO 22 References 28 3 Financial market data 29 3.1 Stylized facts of financial market returns 29 3.1.1 Stylized facts for univariate series 29 3.1.2 Stylized facts for multivariate series 32 3.2 Implications for risk models 35 References 36 4 Measuring risks 37 4.1 Introduction 37 4.2 Synopsis of risk measures 37 4.3 Portfolio risk concepts 42 References 44 5 Modern portfolio theory 46 5.1 Introduction 46 5.2 Markowitz portfolios 47 5.3 Empirical mean-variance portfolios 50 References 52 PART II RISK MODELLING 55 6 Suitable distributions for returns 57 6.1 Preliminaries 57 6.2 The generalized hyperbolic distribution 57 6.3 The generalized lambda distribution 60 6.4 Synopsis of R packages for GHD 66 6.4.1 The package fBasics 66 6.4.2 The package GeneralizedHyperbolic 67 6.4.3 The package ghyp 69 6.4.4 The package QRM 70 6.4.5 The package SkewHyperbolic 70 6.4.6 The package VarianceGamma 71 6.5 Synopsis of R packages for GLD 71 6.5.1 The package Davies 71 6.5.2 The package fBasics 72 6.5.3 The package gld 73 6.5.4 The package lmomco 73 6.6 Applications of the GHD to risk modelling 74 6.6.1 Fitting stock returns to the GHD 74 6.6.2 Risk assessment with the GHD 77 6.6.3 Stylized facts revisited 80 6.7 Applications of the GLD to risk modelling and data analysis 82 6.7.1 VaR for a single stock 82 6.7.2 Shape triangle for FTSE 100 constituents 84 References 86 7 Extreme value theory 89 7.1 Preliminaries 89 7.2 Extreme value methods and models 90 7.2.1 The block maxima approach 90 7.2.2 The rth largest order models 91 7.2.3 The peaks-over-threshold approach 92 7.3 Synopsis of R packages 94 7.3.1 The package evd 94 7.3.2 The package evdbayes 95 7.3.3 The package evir 96 7.3.4 The packages extRemes and in2extRemes 98 7.3.5 The package fExtremes 99 7.3.6 The package ismev 101 7.3.7 The package QRM 101 7.3.8 The packages Renext and RenextGUI 102 7.4 Empirical applications of EVT 103 7.4.1 Section outline 103 7.4.2 Block maxima model for Siemens 103 7.4.3 r-block maxima for BMW 107 7.4.4 POT method for Boeing 110 References 115 8 Modelling volatility 116 8.1 Preliminaries 116 8.2 The class of ARCH models 116 8.3 Synopsis of R packages 120 8.3.1 The package bayesGARCH 120 8.3.2 The package ccgarch 121 8.3.3 The package fGarch 122 8.3.4 The package GEVStableGarch 122 8.3.5 The package gogarch 123 8.3.6 The package lgarch 123 8.3.7 The packages rugarch and rmgarch 125 8.3.8 The package tseries 127 8.4 Empirical application of volatility models 128 References 130 9 Modelling dependence 133 9.1 Overview 133 9.2 Correlation, dependence, and distributions 133 9.3 Copulae 136 9.3.1 Motivation 136 9.3.2 Correlations and dependence revisited 137 9.3.3 Classification of copulae 139 9.4 Synopsis of R packages 142 9.4.1 The package BLCOP 142 9.4.2 The package copula 144 9.4.3 The package fCopulae 146 9.4.4 The package gumbel 147 9.4.5 The package QRM 148 9.5 Empirical applications of copulae 148 9.5.1 GARCH–copula model 148 9.5.2 Mixed copula approaches 155 References 157 PART III PORTFOLIO OPTIMIZATION APPROACHES 161 10 Robust portfolio optimization 163 10.1 Overview 163 10.2 Robust statistics 164 10.2.1 Motivation 164 10.2.2 Selected robust estimators 165 10.3 Robust optimization 168 10.3.1 Motivation 168 10.3.2 Uncertainty sets and problem formulation 168 10.4 Synopsis of R packages 174 10.4.1 The package covRobust 174 10.4.2 The package fPortfolio 174 10.4.3 The package MASS 175 10.4.4 The package robustbase 176 10.4.5 The package robust 176 10.4.6 The package rrcov 178 10.4.7 Packages for solving SOCPs 179 10.5 Empirical applications 180 10.5.1 Portfolio simulation: robust versus classical statistics 180 10.5.2 Portfolio back test: robust versus classical statistics 186 10.5.3 Portfolio back-test: robust optimization 190 References 195 11 Diversification reconsidered 198 11.1 Introduction 198 11.2 Most-diversified portfolio 199 11.3 Risk contribution constrained portfolios 201 11.4 Optimal tail-dependent portfolios 204 11.5 Synopsis of R packages 207 11.5.1 The package cccp 207 11.5.2 The packages DEoptim, DEoptimR, and RcppDE 207 11.5.3 The package FRAPO 210 11.5.4 The package PortfolioAnalytics 211 11.6 Empirical applications 212 11.6.1 Comparison of approaches 212 11.6.2 Optimal tail-dependent portfolio against benchmark 216 11.6.3 Limiting contributions to expected shortfall 221 References 226 12 Risk-optimal portfolios 228 12.1 Overview 228 12.2 Mean-VaR portfolios 229 12.3 Optimal CVaR portfolios 234 12.4 Optimal draw-down portfolios 238 12.5 Synopsis of R packages 241 12.5.1 The package fPortfolio 241 12.5.2 The package FRAPO 243 12.5.3 Packages for linear programming 245 12.5.4 The package PerformanceAnalytics 249 12.6 Empirical applications 251 12.6.1 Minimum-CVaR versus minimum-variance portfolios 251 12.6.2 Draw-down constrained portfolios 254 12.6.3 Back-test comparison for stock portfolio 260 12.6.4 Risk surface plots 265 References 272 13 Tactical asset allocation 274 13.1 Overview 274 13.2 Survey of selected time series models 275 13.2.1 Univariate time series models 275 13.2.2 Multivariate time series models 281 13.3 The Black–Litterman approach 289 13.4 Copula opinion and entropy pooling 292 13.4.1 Introduction 292 13.4.2 The COP model 292 13.4.3 The EP model 293 13.5 Synopsis of R packages 295 13.5.1 The package BLCOP 295 13.5.2 The package dse 297 13.5.3 The package fArma 300 13.5.4 The package forecast 301 13.5.5 The package MSBVAR 302 13.5.6 The package PortfolioAnalytics 304 13.5.7 The packages urca and vars 304 13.6 Empirical applications 307 13.6.1 Black–Litterman portfolio optimization 307 13.6.2 Copula opinion pooling 313 13.6.3 Entropy pooling 318 13.6.4 Protection strategies 324 References 334 14 Probabilistic utility 339 14.1 Overview 339 14.2 The concept of probabilistic utility 340 14.3 Markov chain Monte Carlo 342 14.3.1 Introduction 342 14.3.2 Monte Carlo approaches 343 14.3.3 Markov chains 347 14.3.4 Metropolis–Hastings algorithm 349 14.4 Synopsis of R packages 354 14.4.1 Packages for conducting MCMC 354 14.4.2 Packages for analyzing MCMC 358 14.5 Empirical application 362 14.5.1 Exemplary utility function 362 14.5.2 Probabilistic versus maximized expected utility 366 14.5.3 Simulation of asset allocations 369 References 375 Appendix A Package overview 378 A.1 Packages in alphabetical order 378 A.2 Packages ordered by topic 382 References 386 Appendix B Time series data 391 B.1 Date/time classes 391 B.2 The ts class in the base package stats 395 B.3 Irregularly spaced time series 395 B.4 The package timeSeries 397 B.5 The package zoo 399 B.6 The packages tframe and xts 401 References 404 Appendix C Back-testing and reporting of portfolio strategies 406 C.1 R packages for back-testing 406 C.2 R facilities for reporting 407 C.3 Interfacing with databases 407 References 408 Appendix D Technicalities 411 Reference 411 Index 413

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Book SynopsisA step-by-step approach to problem-solving techniques using SPSS in the fields of sports science and physical education Featuring a clear and accessible approach to the methods, processes, and statistical techniques used in sports science and physical education, Sports Research with Analytical Solution using SPSS emphasizes how to conduct and interpret a range of statistical analysis using SPSS. The book also addresses issues faced by research scholars in these fields by providing analytical solutions to various research problems without reliance on mathematical rigor. Logically arranged to cover both fundamental and advanced concepts, the book presents standard univariate and complex multivariate statistical techniques used in sports research such as multiple regression analysis, discriminant analysis, cluster analysis, and factor analysis. The author focuses on the treatment of various parametric and nonparametric statistical tests, which are shown throTable of ContentsPreface xv About the Companion Website xviii Acknowledgments xix 1 Introduction to Data Types and SPSS Operations 1 1.1 Introduction 1 1.2 Types of data 2 1.2.1 Qualitative Data 2 1.2.2 Quantitative Data 3 1.3 Important definitions 4 1.3.1 Variable 4 1.4 Data Cleaning 4 1.5 Detection of Errors 5 1.5.1 Using Frequencies 5 1.5.2 Using Mean and Standard Deviation 5 1.5.3 Logic Checks 5 1.5.4 Outlier Detection 5 1.6 How to Start Spss? 6 1.6.1 Preparing Data File 7 1.7 Exercise 10 1.7.1 Short Answer Questions 10 1.7.2 Multiple Choice Questions 11 2 Descriptive Profile 14 2.1 Introduction 14 2.2 Explanation of Various Descriptive Statistics 16 2.2.1 Mean 16 2.2.2 Variance 16 2.2.3 Standard Error of Mean 17 2.2.4 Skewness 17 2.2.5 Kurtosis 18 2.2.6 Percentiles 19 2.3 Application of Descriptive Statistics 19 2.3.1 Testing Normality of Data and Identifying Outliers 20 2.4 Computation of Descriptive Statistics Using Spss 25 2.4.1 Preparation of Data File 25 2.4.2 Defining Variables 26 2.4.3 Entering Data 26 2.4.4 SPSS Commands 26 2.5 Interpretations of the Results 29 2.6 Developing Profile Chart 31 2.7 Summary of Spss Commands 33 2.8 Exercise 33 2.8.1 Short Answer Questions 33 2.8.2 Multiple Choice Questions 34 2.9 Case Study on Descriptive Analysis 36 3 Correlation Coefficient and Partial Correlation 41 3.1 Introduction 41 3.2 Correlation Matrix and Partial Correlation 43 3.2.1 Product Moment Correlation Coefficient 43 3.2.2 Partial Correlation 45 3.3 Application of Correlation Matrix and Partial Correlation 46 3.4 Correlation Matrix with Spss 46 3.4.1 Computation in Correlation Matrix 46 3.4.2 Interpretations of Findings 51 3.5 Partial Correlation with Spss 51 3.5.1 Computation of Partial Correlations 52 3.5.2 Interpretation of Partial Correlation 55 3.6 Summary of the Spss Commands 56 3.6.1 For Computing Correlation Matrix 56 3.6.2 For Computing Partial Correlations 57 3.7 Exercise 57 3.7.1 Short Answer Questions 57 3.7.2 Multiple Choice Questions 57 3.7.3 Assignment 60 3.8 Case Study on Correlation 60 4 Comparing Means 65 4.1 Introduction 65 4.2 One‐Sample t‐Test 66 4.2.1 Application of One‐Sample t‐Test 67 4.3 Two‐Sample t‐Test for Unrelated Groups 67 4.3.1 Assumptions While Using t‐Test 67 4.3.2 Case I: Two‐Tailed Test 68 4.3.3 Case II: Right Tailed Test 68 4.3.4 Case III: Left Tailed Test 69 4.3.5 Application of Two‐Sample t-Test 70 4.4 Paired t‐Test for Related Groups 70 4.4.1 Case I: Two‐Tailed Test 71 4.4.2 Case II: Right Tailed Test 71 4.4.3 Case III: Left Tailed Test 72 4.4.4 Application of Paired t‐Test 73 4.5 One‐Sample t‐Test with Spss 73 4.5.1 Computation in t‐Test for Single Group 74 4.5.2 Interpretation of Findings 77 4.6 Two‐Sample t‐Test for Independent Groups with Spss 78 4.6.1 Computation in Two‐Sample t‐Test 79 4.6.2 Interpretation of Findings 83 4.7 Paired t‐Test for Related Groups with Spss 85 4.7.1 Computation in Paired t‐Test 86 4.7.2 Interpretation of Findings 89 4.8 Summary of Spss Commands for t‐Tests 90 4.8.1 One‐Sample t‐Test 90 4.8.2 Two‐Sample t‐Test for Independent Groups 90 4.8.3 Paired t‐Test 91 4.9 Exercise 91 4.9.1 Short Answer Questions 91 4.9.2 Multiple Choice Questions 91 4.9.3 Assignment 93 4.10 Case Study 94 5 Independent Measures Anova 100 5.1 Introduction 101 5.2 One‐Way Analysis of Variance 101 5.2.1 One‐Way ANOVA Model 102 5.2.2 Post Hoc Test 102 5.2.3 Application of One‐Way ANOVA 103 5.3 One‐Way Anova with Spss (Equal Sample Size) 103 5.3.1 Computation in One‐Way ANOVA (Equal Sample Size) 104 5.3.2 Interpretation of Findings 107 5.4 One‐Way Anova with Spss (Unequal Sample Size) 110 5.4.1 Computation in One‐Way ANOVA (Unequal Sample Size) 111 5.4.2 Interpretation of Findings 114 5.5 Two‐Way Analysis of Variance 115 5.5.1 Assumptions in Two‐Way Analysis of Variance 116 5.5.2 Hypotheses in Two‐Way ANOVA 116 5.5.3 Factors 117 5.5.4 Treatment Groups 117 5.5.5 Main Effect 117 5.5.6 Interaction Effect 117 5.5.7 Within‐Groups Variation 117 5.5.8 F‐Statistic 117 5.5.9 Two‐Way ANOVA Table 118 5.5.10 Interpretation 118 5.5.11 Application of Two‐Way Analysis of Variance 118 5.6 Two‐Way Anova Using Spss 119 5.6.1 Computation in Two‐Way ANOVA 121 5.6.2 Interpretation of Findings 126 5.7 Summary of the Spss Commands 137 5.7.1 One‐Way ANOVA 137 5.7.2 Two‐Way ANOVA 138 5.8 Exercise 138 5.8.1 Short Answer Questions 138 5.8.2 Multiple Choice Questions 139 5.8.3 Assignment 142 5.9 Case Study on One‐Way Anova Design 143 5.10 Case Study on Two‐Way Anova 147 6 Repeated Measures Anova 153 6.1 Introduction 153 6.2 One‐Way Repeated Measures Anova 154 6.2.1 Assumptions in One‐Way Repeated Measures ANOVA 155 6.2.2 Application in Sports Research 155 6.2.3 Steps in Solving One‐Way Repeated Measures ANOVA 156 6.3 One‐Way Repeated Measures Anova Using Spss 157 6.3.1 Computation in the One‐Way Repeated Measures ANOVA 157 6.3.2 Interpretation of Findings 161 6.3.3 Findings of the Study 165 6.3.4 Inference 166 6.4 Two‐Way Repeated Measures Anova 166 6.4.1 Assumptions in Two‐Way Repeated Measures ANOVA 166 6.4.2 Application in Sports Research 167 6.4.3 Steps in Solving Two‐Way Repeated Measures ANOVA 167 6.5 Two‐Way Repeated Measures Anova Using Spss 168 6.5.1 Computation in Two‐Way Repeated Measures ANOVA 170 6.5.2 Interpretation of Findings 173 6.5.3 Findings of the Study 181 6.5.4 Inference 181 6.6 Summary of the Spss Commands for One‐Way Repeated Measures Anova 182 6.7 Summary of the Spss Commands for Two‐Way Repeated Measures Anova 182 6.8 Exercise 183 6.8.1 Short Answer Questions 183 6.8.2 Multiple Choice Questions 183 6.8.3 Assignment 185 6.9 Case Study on Repeated Measures Design 186 7 Analysis of Covariance 190 7.1 Introduction 190 7.2 Conceptual Framework of Analysis of Covariance 191 7.3 Application of ANCOVA 192 7.4 ANCOVA with Spss 193 7.4.1 Computation in ANCOVA 194 7.5 Summary of the Spss Commands 201 7.6 Exercise 202 7.6.1 Short Answer Questions 202 7.6.2 Multiple Choice Questions 202 7.6.3 Assignment 203 7.7 Case Study on ANCOVA Design 204 8 Nonparametric Tests in Sports Research 209 8.1 Introduction 209 8.2 Chi‐Square Test 211 8.2.1 Testing Goodness of Fit 211 8.2.2 Yates’ Correction 212 8.2.3 Contingency Coefficient 212 8.3 Goodness of Fit with Spss 212 8.3.1 Computation in Goodness of Fit 213 8.3.2 Interpretation of Findings 216 8.4 Testing Independence of Two Attributes 216 8.4.1 Interpretation 218 8.5 Testing Association with Spss 219 8.5.1 Computation in Chi‐Square 219 8.5.2 Interpretation of Findings 223 8.6 Mann–Whitney U Test: Comparing Two Independent Samples 224 8.6.1 Computation in Mann–Whitney U Statistic Using SPSS 224 8.6.2 Interpretation of Findings 226 8.7 Wilcoxon Signed‐Rank Test: For Comparing Two Related Groups 227 8.7.1 Computation in Wilcoxon Signed‐Rank Test Using SPSS 228 8.7.2 Interpretation of Findings 230 8.8 Kruskal–Wallis Test 231 8.8.1 Computation in Kruskal–Wallis Test Using SPSS 232 8.8.2 Interpretation of Findings 234 8.9 Friedman Test 234 8.9.1 Computation in Friedman Test Using SPSS 235 8.9.2 Interpretation of Findings 237 8.10 Summary of the Spss Commands 237 8.10.1 Computing Chi‐Square Statistic (for Testing Goodness of Fit) 237 8.10.2 Computing Chi‐Square Statistic (for Testing Independence) 238 8.10.3 Computation in Mann–Whitney U Test 238 8.10.4 Computation in Wilcoxon Signed‐Rank Test 239 8.10.5 Computation in Kruskal–Wallis Test 239 8.10.6 Computation in Friedman Test 239 8.11 Exercise 240 8.11.1 Short Answer Questions 240 8.11.2 Multiple Choice Questions 241 8.11.3 Assignment 243 8.12 Case Study on Testing Independence of Attributes 243 9 Regression Analysis and Multiple Correlations 246 9.1 Introduction 246 9.2 Understanding Regression Equation 247 9.2.1 Methods of Regression Analysis 247 9.2.2 Multiple Correlation 248 9.3 Application of Regression Analysis 248 9.4 Multiple Regression Analysis with Spss 249 9.4.1 Computation in Regression Analysis 249 9.4.2 Interpretation of Findings 254 9.5 Summary of Spss Commands for Regression Analysis 259 9.6 Exercise 259 9.6.1 Short Answer Questions 259 9.6.2 Multiple Choice Questions 260 9.6.3 Assignment 261 9.7 Case Study on Regression Analysis 263 10 Application of Discriminant Function Analysis 267 10.1 Introduction 268 10.2 Basics of Discriminant Function Analysis 268 10.2.1 Discriminating Variables 268 10.2.2 Dependent Variable 268 10.2.3 Discriminant Function 268 10.2.4 Classification Matrix 269 10.2.5 Stepwise Method of Discriminant Analysis 269 10.2.6 Power of Discriminating Variable 269 10.2.7 Canonical Correlation 269 10.2.8 Wilks’ Lambda 270 10.3 Assumptions in Discriminant Analysis 270 10.4 Why to Use Discriminant Analysis 270 10.5 Steps in Discriminant Analysis 271 10.6 Application of Discriminant Function Analysis 272 10.7 Discriminant Analysis Using Spss 274 10.7.1 Computation in Discriminant Analysis 274 10.7.2 Interpretation of Findings 279 10.8 Summary of the Spss Commands for Discriminant Analysis 284 10.9 Exercise 284 10.9.1 Short Answer Questions 284 10.9.2 Multiple Choice Questions 285 10.9.3 Assignment 286 10.10 Case Study on Discriminant Analysis 288 11 Logistic Regression for Developing Logit Model in Sport 293 11.1 Introduction 293 11.2 Understanding Logistic Regression 294 11.3 Application of Logistic Regression in Sports Research 295 11.4 Assumptions in Logistic Regression 297 11.5 Steps in Developing Logistic Model 297 11.6 Logistic Analysis Using Spss 297 11.6.1 Block 0 299 11.6.2 Block 1 299 11.6.3 Computation in Logistic Regression with SPSS 299 11.7 Interpretation of Findings 304 11.7.1 Case Processing and Coding Summary 304 11.7.2 Analyzing Logistic Models 305 11.8 Summary of the Spss Commands for Logistic Regression 310 11.9 Exercise 310 11.9.1 Short Answer Questions 310 11.9.2 Multiple Choice Questions 311 11.9.3 Assignment 312 11.10 Case Study on Logistic Regression 313 12 Application of Factor Analysis 319 12.1 Introduction 319 12.2 Terminologies Used in Factor Analysis 320 12.2.1 Principal Component Analysis 320 12.2.2 Eigenvalue 320 12.2.3 Kaiser Criterion 321 12.2.4 The Scree Test 321 12.2.5 Communality 321 12.2.6 Factor Loading 322 12.2.7 Varimax Rotation 322 12.3 Assumptions in Factor Analysis 322 12.4 Steps in Factor Analysis 323 12.5 Application of Factor Analysis 323 12.6 Factor Analysis with Spss 324 12.6.1 Computation in Factor Analysis Using SPSS 326 12.7 Summary of the Spss Commands for Factor Analysis 336 12.8 Exercise 336 12.8.1 Short Answer Questions 336 12.8.2 Multiple Choice Questions 337 12.8.3 Assignment 338 12.9 Case Study on Factor Analysis 339 Appendix 346 Bibliography 360 Index 368

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Book SynopsisDemonstrates the simplicity and effectiveness of Mathematica as the solution to practical problems in composite materials. Designed for those who need to learn how micromechanical approaches can help understand the behaviour of bodies with voids, inclusions, defects, this book is perfect for readers without a programming background.Table of ContentsPreface ix About the Companion Website xi 1 Coordinate Transformation and Tensors 1 1.1 Index Notation 1 1.1.1 Some Examples of Index Notation in 3-D 3 1.1.2 Mathematica Implementation 3 1.1.3 Kronecker Delta 6 1.1.4 Permutation Symbols 9 1.1.5 Product of Matrices 10 1.2 Coordinate Transformations (Cartesian Tensors) 11 1.3 Definition of Tensors 13 1.3.1 Tensor of Rank 0 (Scalar) 13 1.3.2 Tensor of Rank 1 (Vector) 14 1.3.3 Tensor of Rank 2 15 1.3.4 Tensor of Rank 3 17 1.3.5 Tensor of Rank 4 17 1.3.6 Differentiation 19 1.3.7 Differentiation of Cartesian Tensors 20 1.4 Invariance of Tensor Equations 21 1.5 Quotient Rule 22 1.6 Exercises 23 References 24 2 Field Equations 25 2.1 Concept of Stress 25 2.1.1 Properties of Stress 29 2.1.2 (Stress) Boundary Conditions 30 2.1.3 Principal Stresses 31 2.1.4 Stress Deviator 35 2.1.5 Mohr’s Circle 38 2.2 Strain 40 2.2.1 Shear Deformation 47 2.3 Compatibility Condition 49 2.4 Constitutive Relation, Isotropy, Anisotropy 50 2.4.1 Isotropy 52 2.4.2 Elastic Modulus 54 2.4.3 Orthotropy 56 2.4.4 2-D Orthotropic Materials 57 2.4.5 Transverse Isotropy 57 2.5 Constitutive Relation for Fluids 58 2.5.1 Thermal Effect 58 2.6 Derivation of Field Equations 59 2.6.1 Divergence Theorem (Gauss Theorem) 59 2.6.2 Material Derivative 60 2.6.3 Equation of Continuity 62 2.6.4 Equation of Motion 62 2.6.5 Equation of Energy 63 2.6.6 Isotropic Solids 65 2.6.7 Isotropic Fluids 65 2.6.8 Thermal Effects 66 2.7 General Coordinate System 66 2.7.1 Introduction to Tensor Analysis 66 2.7.2 Definition of Tensors in Curvilinear Systems 68 2.7.3 Metric Tensor10, gij 69 2.7.4 Covariant Derivatives 70 2.7.5 Examples 73 2.7.6 Vector Analysis 75 2.8 Exercises 77 References 80 3 Inclusions in Infinite Media 81 3.1 Eshelby’s Solution for an Ellipsoidal Inclusion Problem 82 3.1.1 Eigenstrain Problem 85 3.1.2 Eshelby Tensors for an Ellipsoidal Inclusion 87 3.1.3 Inhomogeneity (Inclusion) Problem 95 3.2 Multilayered Inclusions 104 3.2.1 Background 104 3.2.2 Implementation of Index Manipulation in Mathematica 105 3.2.3 General Formulation 108 3.2.4 Exact Solution for Two-Phase Materials 116 3.2.5 Exact Solution for Three-Phase Materials 123 3.2.6 Exact Solution for Four-Phase Materials 132 3.2.7 Exact Solution for 2-D Multiphase Materials 137 3.3 Thermal Stress 137 3.3.1 Thermal Stress Due to Heat Source 138 3.3.2 Thermal Stress Due to Heat Flow 146 3.4 Airy’s Stress Function Approach 155 3.4.1 Airy’s Stress Function 156 3.4.2 Mathematica Programming of Complex Variables 161 3.4.3 Multiphase Inclusion Problems Using Airy’s Stress Function 163 3.5 Effective Properties 172 3.5.1 Upper and Lower Bounds of Effective Properties 173 3.5.2 Self-Consistent Approximation 175 3.5.3 Source Code for micromech.m 178 3.6 Exercises 188 References 189 4 Inclusions in Finite Matrix 191 4.1 General Approaches for Numerically Solving Boundary Value Problems 192 4.1.1 Method of Weighted Residuals 192 4.1.2 Rayleigh–Ritz Method 203 4.1.3 Sturm–Liouville System 205 4.2 Steady-State Heat Conduction Equations 213 4.2.1 Derivation of Permissible Functions 213 4.2.2 Finding Temperature Field Using Permissible Functions 227 4.3 Elastic Fields with Bounded Boundaries 232 4.4 Numerical Examples 238 4.4.1 Homogeneous Medium 238 4.4.2 Single Inclusion 240 4.5 Exercises 251 References 252 Appendix A Introduction to Mathematica 253 A.1 Essential Commands/Statements 255 A.2 Equations 256 A.3 Differentiation/Integration 260 A.4 Matrices/Vectors/Tensors 260 A.5 Functions 262 A.6 Graphics 263 A.7 Other Useful Functions 265 A.8 Programming in Mathematica 267 A.8.1 Control Statements 268 A.8.2 Tensor Manipulations 270 References 272 Index 273

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Book SynopsisThis introductory text walks readers from the fundamental mechanics of lift to the stage of being able to make practical calculations and predictions of the coefficient of lift for realistic wing profile and platform geometries.Trade Review“This book is a very useful digest of key points from the literature, carefully structured and presented with helpful pointers as to how the successive aerodynamical models can be implemented in the ‘now so readily available interactive matrix computation systems.” (Aeronautical Journal, 1 August 2013)Table of ContentsPreface xvii Series Preface xxiii Part One Plane Ideal Aerodynamics 1 Preliminary Notions 3 1.1 Aerodynamic Force and Moment 3 1.1.1 Motion of the Frame of Reference 3 1.1.2 Orientation of the System of Coordinates 4 1.1.3 Components of the Aerodynamic Force 4 1.1.4 Formulation of the Aerodynamic Problem 4 1.2 Aircraft Geometry 5 1.2.1 Wing Section Geometry 6 1.2.2 Wing Geometry 7 1.3 Velocity 8 1.4 Properties of Air 8 1.4.1 Equation of State: Compressibility and the Speed of Sound 8 1.4.2 Rheology: Viscosity 10 1.4.3 The International Standard Atmosphere 12 1.4.4 Computing Air Properties 12 1.5 Dimensional Theory 13 1.5.1 Alternative methods 16 1.5.2 Example: Using Octave to Solve a Linear System 16 1.6 Example: NACA Report No. 502 18 1.7 Exercises 19 1.8 Further Reading 22 References 22 2 Plane Ideal Flow 25 2.1 Material Properties: The Perfect Fluid 25 2.2 Conservation of Mass 26 2.2.1 Governing Equations: Conservation Laws 26 2.3 The Continuity Equation 26 2.4 Mechanics: The Euler Equations 27 2.4.1 Rate of Change of Momentum 27 2.4.2 Forces Acting on a Fluid Particle 28 2.4.3 The Euler Equations 29 2.4.4 Accounting for Conservative External Forces 29 2.5 Consequences of the Governing Equations 30 2.5.1 The Aerodynamic Force 30 2.5.2 Bernoulli’s Equation 33 2.5.3 Circulation, Vorticity, and Irrotational Flow 33 2.5.4 Plane Ideal Flows 35 2.6 The Complex Velocity 35 2.6.1 Review of Complex Variables 35 2.6.2 Analytic Functions and Plane Ideal Flow 38 2.6.3 Example: the Polar Angle Is Nowhere Analytic 40 2.7 The Complex Potential 41 2.8 Exercises 42 2.9 Further Reading 44 References 45 3 Circulation and Lift 47 3.1 Powers of z 47 3.1.1 Divergence and Vorticity in Polar Coordinates 48 3.1.2 Complex Potentials 48 3.1.3 Drawing Complex Velocity Fields with Octave 49 3.1.4 Example: k = 1, Corner Flow 50 3.1.5 Example: k = 0, Uniform Stream 51 3.1.6 Example: k =−1, Source 51 3.1.7 Example: k =−2, Doublet 52 3.2 Multiplication by a Complex Constant 53 3.2.1 Example: w = const., Uniform Stream with Arbitrary Direction 53 3.2.2 Example: w = i/z, Vortex 54 3.2.3 Example: Polar Components 54 3.3 Linear Combinations of Complex Velocities 54 3.3.1 Example: Circular Obstacle in a Stream 54 3.4 Transforming the Whole Velocity Field 56 3.4.1 Translating the Whole Velocity Field 56 3.4.2 Example: Doublet as the Sum of a Source and Sink 56 3.4.3 Rotating the Whole Velocity Field 56 3.5 Circulation and Outflow 57 3.5.1 Curve-integrals in Plane Ideal Flow 57 3.5.2 Example: Numerical Line-integrals for Circulation and Outflow 58 3.5.3 Closed Circuits 59 3.5.4 Example: Powers of z and Circles around the Origin 60 3.6 More on the Scalar Potential and Stream Function 61 3.6.1 The Scalar Potential and Irrotational Flow 61 3.6.2 The Stream Function and Divergence-free Flow 62 3.7 Lift 62 3.7.1 Blasius’s Theorem 62 3.7.2 The Kutta–Joukowsky Theorem 63 3.8 Exercises 64 3.9 Further Reading 65 References 66 4 Conformal Mapping 67 4.1 Composition of Analytic Functions 67 4.2 Mapping with Powers of ζ 68 4.2.1 Example: Square Mapping 68 4.2.2 Conforming Mapping by Contouring the Stream Function 69 4.2.3 Example: Two-thirds Power Mapping 69 4.2.4 Branch Cuts 70 4.2.5 Other Powers 71 4.3 Joukowsky’s Transformation 71 4.3.1 Unit Circle from a Straight Line Segment 71 4.3.2 Uniform Flow and Flow over a Circle 72 4.3.3 Thin Flat Plate at Nonzero Incidence 73 4.3.4 Flow over the Thin Flat Plate with Circulation 74 4.3.5 Joukowsky Aerofoils 75 4.4 Exercises 75 4.5 Further Reading 78 References 78 5 Flat Plate Aerodynamics 79 5.1 Plane Ideal Flow over a Thin Flat Plate 79 5.1.1 Stagnation Points 80 5.1.2 The Kutta–Joukowsky Condition 80 5.1.3 Lift on a Thin Flat Plate 81 5.1.4 Surface Speed Distribution 82 5.1.5 Pressure Distribution 83 5.1.6 Distribution of Circulation 84 5.1.7 Thin Flat Plate as Vortex Sheet 85 5.2 Application of Thin Aerofoil Theory to the Flat Plate 87 5.2.1 Thin Aerofoil Theory 87 5.2.2 Vortex Sheet along the Chord 87 5.2.3 Changing the Variable of Integration 88 5.2.4 Glauert’s Integral 88 5.2.5 The Kutta–Joukowsky Condition 89 5.2.6 Circulation and Lift 89 5.3 Aerodynamic Moment 89 5.3.1 Centre of Pressure and Aerodynamic Centre 90 5.4 Exercises 90 5.5 Further Reading 91 References 91 6 Thin Wing Sections 93 6.1 Thin Aerofoil Analysis 93 6.1.1 Vortex Sheet along the Camber Line 93 6.1.2 The Boundary Condition 93 6.1.3 Linearization 94 6.1.4 Glauert’s Transformation 95 6.1.5 Glauert’s Expansion 95 6.1.6 Fourier Cosine Decomposition of the Camber Line Slope 97 6.2 Thin Aerofoil Aerodynamics 98 6.2.1 Circulation and Lift 98 6.2.2 Pitching Moment about the Leading Edge 99 6.2.3 Aerodynamic Centre 100 6.2.4 Summary 101 6.3 Analytical Evaluation of Thin Aerofoil Integrals 101 6.3.1 Example: the NACA Four-digit Wing Sections 104 6.4 Numerical Thin Aerofoil Theory 105 6.5 Exercises 109 6.6 Further Reading 109 References 109 7 Lumped Vortex Elements 111 7.1 The Thin Flat Plate at Arbitrary Incidence, Again 111 7.1.1 Single Vortex 111 7.1.2 The Collocation Point 111 7.1.3 Lumped Vortex Model of the Thin Flat Plate 112 7.2 Using Two Lumped Vortices along the Chord 114 7.2.1 Postprocessing 116 7.3 Generalization to Multiple Lumped Vortex Panels 117 7.3.1 Postprocessing 117 7.4 General Considerations on Discrete Singularity Methods 117 7.5 Lumped Vortex Elements for Thin Aerofoils 119 7.5.1 Panel Chains for Camber Lines 119 7.5.2 Implementation in Octave 121 7.5.3 Comparison with Thin Aerofoil Theory 122 7.6 Disconnected Aerofoils 123 7.6.1 Other Applications 124 7.7 Exercises 125 7.8 Further Reading 125 References 126 8 Panel Methods for Plane Flow 127 8.1 Development of the CUSSSP Program 127 8.1.1 The Singularity Elements 127 8.1.2 Discretizing the Geometry 129 8.1.3 The Influence Matrix 131 8.1.4 The Right-hand Side 132 8.1.5 Solving the Linear System 134 8.1.6 Postprocessing 135 8.2 Exercises 137 8.2.1 Projects 138 8.3 Further Reading 139 References 139 8.4 Conclusion to Part I: The Origin of Lift 139 Part Two Three-dimensional Ideal Aerodynamics 9 Finite Wings and Three-Dimensional Flow 143 9.1 Wings of Finite Span 143 9.1.1 Empirical Effect of Finite Span on Lift 143 9.1.2 Finite Wings and Three-dimensional Flow 143 9.2 Three-Dimensional Flow 145 9.2.1 Three-dimensional Cartesian Coordinate System 145 9.2.2 Three-dimensional Governing Equations 145 9.3 Vector Notation and Identities 145 9.3.1 Addition and Scalar Multiplication of Vectors 145 9.3.2 Products of Vectors 146 9.3.3 Vector Derivatives 147 9.3.4 Integral Theorems for Vector Derivatives 148 9.4 The Equations Governing Three-Dimensional Flow 149 9.4.1 Conservation of Mass and the Continuity Equation 149 9.4.2 Newton’s Law and Euler’s Equation 149 9.5 Circulation 150 9.5.1 Definition of Circulation in Three Dimensions 150 9.5.2 The Persistence of Circulation 151 9.5.3 Circulation and Vorticity 151 9.5.4 Rotational Form of Euler’s Equation 153 9.5.5 Steady Irrotational Motion 153 9.6 Exercises 154 9.7 Further Reading 155 References 155 10 Vorticity and Vortices 157 10.1 Streamlines, Stream Tubes, and Stream Filaments 157 10.1.1 Streamlines 157 10.1.2 Stream Tubes and Stream Filaments 158 10.2 Vortex Lines, Vortex Tubes, and Vortex Filaments 159 10.2.1 Strength of Vortex Tubes and Filaments 159 10.2.2 Kinematic Properties of Vortex Tubes 159 10.3 Helmholtz’s Theorems 159 10.3.1 ‘Vortex Tubes Move with the Flow’ 159 10.3.2 ‘The Strength of a Vortex Tube is Constant’ 160 10.4 Line Vortices 160 10.4.1 The Two-dimensional Vortex 160 10.4.2 Arbitrarily Oriented Rectilinear Vortex Filaments 160 10.5 Segmented Vortex Filaments 161 10.5.1 The Biot–Savart Law 161 10.5.2 Rectilinear Vortex Filaments 162 10.5.3 Finite Rectilinear Vortex Filaments 164 10.5.4 Infinite Straight Line Vortices 164 10.5.5 Semi-infinite Straight Line Vortex 164 10.5.6 Truncating Infinite Vortex Segments 165 10.5.7 Implementing Line Vortices in Octave 165 10.6 Exercises 166 10.7 Further Reading 167 References 167 11 Lifting Line Theory 169 11.1 Basic Assumptions of Lifting Line Theory 169 11.2 The Lifting Line, Horseshoe Vortices, and the Wake 169 11.2.1 Deductions from Vortex Theorems 169 11.2.2 Deductions from the Wing Pressure Distribution 170 11.2.3 The Lifting Line Model of Air Flow 170 11.2.4 Horseshoe Vortex 170 11.2.5 Continuous Trailing Vortex Sheet 171 11.2.6 The Form of the Wake 172 11.3 The Effect of Downwash 173 11.3.1 Effect on the Angle of Incidence: Induced Incidence 173 11.3.2 Effect on the Aerodynamic Force: Induced Drag 174 11.4 The Lifting Line Equation 174 11.4.1 Glauert’s Solution of the Lifting Line Equation 175 11.4.2 Wing Properties in Terms of Glauert’s Expansion 176 11.5 The Elliptic Lift Loading 178 11.5.1 Properties of the Elliptic Lift Loading 179 11.6 Lift–Incidence Relation 180 11.6.1 Linear Lift–Incidence Relation 181 11.7 Realizing the Elliptic Lift Loading 182 11.7.1 Corrections to the Elliptic Loading Approximation 182 11.8 Exercises 182 11.9 Further Reading 183 References 183 12 Nonelliptic Lift Loading 185 12.1 Solving the Lifting Line Equation 185 12.1.1 The Sectional Lift–Incidence Relation 185 12.1.2 Linear Sectional Lift–Incidence Relation 185 12.1.3 Finite Approximation: Truncation and Collocation 185 12.1.4 Computer Implementation 187 12.1.5 Example: a Rectangular Wing 187 12.2 Numerical Convergence 188 12.3 Symmetric Spanwise Loading 189 12.3.1 Example: Exploiting Symmetry 191 12.4 Exercises 192 References 192 13 Lumped Horseshoe Elements 193 13.1 A Single Horseshoe Vortex 193 13.1.1 Induced Incidence of the Lumped Horseshoe Element 195 13.2 Multiple Horseshoes along the Span 195 13.2.1 A Finite-step Lifting Line in Octave 197 13.3 An Improved Discrete Horseshoe Model 200 13.4 Implementing Horseshoe Vortices in Octave 203 13.4.1 Example: Yawed Horseshoe Vortex Coefficients 205 13.5 Exercises 206 13.6 Further Reading 207 References 207 14 The Vortex Lattice Method 209 14.1 Meshing the Mean Lifting Surface of a Wing 209 14.1.1 Plotting the Mesh of a Mean Lifting Surface 210 14.2 A Vortex Lattice Method 212 14.2.1 The Vortex Lattice Equations 213 14.2.2 Unit Normals to the Vortex-lattice 215 14.2.3 Spanwise Symmetry 215 14.2.4 Postprocessing Vortex Lattice Methods 215 14.3 Examples of Vortex Lattice Calculations 216 14.3.1 Campbell’s Flat Swept Tapered Wing 216 14.3.2 Bertin’s Flat Swept Untapered Wing 218 14.3.3 Spanwise and Chordwise Refinement 219 14.4 Exercises 220 14.5 Further Reading 221 14.5.1 Three-dimensional Panel Methods 222 References 222 Part Three Nonideal Flow in Aerodynamics 15 Viscous Flow 225 15.1 Cauchy’s First Law of Continuum Mechanics 225 15.2 Rheological Constitutive Equations 227 15.2.1 Perfect Fluid 227 15.2.2 Linearly Viscous Fluid 227 15.3 The Navier–Stokes Equations 228 15.4 The No-Slip Condition and the Viscous Boundary Layer 228 15.5 Unidirectional Flows 229 15.5.1 Plane Couette and Poiseuille Flows 229 15.6 A Suddenly Sliding Plate 230 15.6.1 Solution by Similarity Variable 230 15.6.2 The Diffusion of Vorticity 233 15.7 Exercises 234 15.8 Further Reading 234 References 235 16 Boundary Layer Equations 237 16.1 The Boundary Layer over a Flat Plate 237 16.1.1 Scales in the Conservation of Mass 237 16.1.2 Scales in the Streamwise Momentum Equation 238 16.1.3 The Reynolds Number 239 16.1.4 Pressure in the Boundary Layer 239 16.1.5 The Transverse Momentum Balance 239 16.1.6 The Boundary Layer Momentum Equation 240 16.1.7 Pressure and External Tangential Velocity 241 16.1.8 Application to Curved Surfaces 241 16.2 Momentum Integral Equation 241 16.3 Local Boundary Layer Parameters 243 16.3.1 The Displacement and Momentum Thicknesses 243 16.3.2 The Skin Friction Coefficient 243 16.3.3 Example: Three Boundary Layer Profiles 244 16.4 Exercises 248 16.5 Further Reading 249 References 249 17 Laminar Boundary Layers 251 17.1 Boundary Layer Profile Curvature 251 17.1.1 Pressure Gradient and Boundary Layer Thickness 252 17.2 Pohlhausen’s Quartic Profiles 252 17.3 Thwaites’s Method for Laminar Boundary Layers 254 17.3.1 F(λ) ≈ 0.45 − 6λ 255 17.3.2 Correlations for Shape Factor and Skin Friction 256 17.3.3 Example: Zero Pressure Gradient 256 17.3.4 Example: Laminar Separation from a Circular Cylinder 257 17.4 Exercises 260 17.5 Further Reading 261 References 262 18 Compressibility 263 18.1 Steady-State Conservation of Mass 263 18.2 Longitudinal Variation of Stream Tube Section 265 18.2.1 The Design of Supersonic Nozzles 266 18.3 Perfect Gas Thermodynamics 266 18.3.1 Thermal and Caloric Equations of State 266 18.3.2 The First Law of Thermodynamics 267 18.3.3 The Isochoric and Isobaric Specific Heat Coefficients 267 18.3.4 Isothermal and Adiabatic Processes 267 18.3.5 Adiabatic Expansion 268 18.3.6 The Speed of Sound and Temperature 269 18.3.7 The Speed of Sound and the Speed 269 18.3.8 Thermodynamic Characteristics of Air 270 18.3.9 Example: Stagnation Temperature 270 18.4 Exercises 270 18.5 Further Reading 271 References 271 19 Linearized Compressible Flow 273 19.1 The Nonlinearity of the Equation for the Potential 273 19.2 Small Disturbances to the Free-Stream 274 19.3 The Uniform Free-Stream 275 19.4 The Disturbance Potential 275 19.5 Prandtl–Glauert Transformation 276 19.5.1 Fundamental Linearized Compressible Flows 277 19.5.2 The Speed of Sound 278 19.6 Application of the Prandtl–Glauert Rule 279 19.6.1 Transforming the Geometry 279 19.6.2 Computing Aerodynamical Forces 280 19.6.3 The Prandlt–Glauert Rule in Two Dimensions 282 19.6.4 The Critical Mach Number 284 19.7 Sweep 284 19.8 Exercises 285 19.9 Further Reading 285 References 286 Appendix A Notes on Octave Programming 287 A. 1 Introduction 287 A. 2 Vectorization 287 A.2. 1 Iterating Explicitly 288 A.2. 2 Preallocating Memory 288 A.2. 3 Vectorizing Function Calls 288 A.2. 4 Many Functions Act Elementwise on Arrays 289 A.2. 5 Functions Primarily Defined for Arrays 289 A.2. 6 Elementwise Arithmetic with Single Numbers 289 A.2. 7 Elementwise Arithmetic between Arrays 290 A.2. 8 Vector and Matrix Multiplication 290 A. 3 Generating Arrays 290 A.3. 1 Creating Tables with bsxfun 290 A. 4 Indexing 291 A.4. 1 Indexing by Logical Masks 291 A.4. 2 Indexing Numerically 291 A. 5 Just-in-Time Compilation 291 A. 6 Further Reading 292 References 292 Glossary 293 Nomenclature 305 Index 309

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Book SynopsisA comprehensive introduction to various numerical methods used in computational finance today Quantitative skills are a prerequisite for anyone working in finance or beginning a career in the field, as well as risk managers. A thorough grounding in numerical methods is necessary, as is the ability to assess their quality, advantages, and limitations. This book offers a thorough introduction to each method, revealing the numerical traps that practitioners frequently fall into. Each method is referenced with practical, real-world examples in the areas of valuation, risk analysis, and calibration of specific financial instruments and models. It features a strong emphasis on robust schemes for the numerical treatment of problems within computational finance. Methods covered include PDE/PIDE using finite differences or finite elements, fast and stable solvers for sparse grid systems, stabilization and regularization techniques for inverse problems resulting from the calibration oTable of ContentsAcknowledgements xiii About the Authors xv 1 Introduction and Reading Guide 1 2 Binomial Trees 7 2.1 Equities and Basic Options 7 2.2 The One Period Model 8 2.3 The Multiperiod Binomial Model 9 2.4 Black-Scholes and Trees 10 2.5 Strengths and Weaknesses of Binomial Trees 12 2.5.1 Ease of Implementation 12 2.5.2 Oscillations 12 2.5.3 Non-recombining Trees 14 2.5.4 Exotic Options and Trees 14 2.5.5 Greeks and Binomial Trees 15 2.5.6 Grid Adaptivity and Trees 15 2.6 Conclusion 16 3 Finite Differences and the Black-Scholes PDE 17 3.1 A Continuous Time Model for Equity Prices 17 3.2 Black-Scholes Model: From the SDE to the PDE 19 3.3 Finite Differences 23 3.4 Time Discretization 27 3.5 Stability Considerations 30 3.6 Finite Differences and the Heat Equation 30 3.6.1 Numerical Results 34 3.7 Appendix: Error Analysis 36 4 Mean Reversion and Trinomial Trees 39 4.1 Some Fixed Income Terms 39 4.1.1 Interest Rates and Compounding 39 4.1.2 Libor Rates and Vanilla Interest Rate Swaps 40 4.2 Black76 for Caps and Swaptions 43 4.3 One-Factor Short Rate Models 45 4.3.1 Prominent Short Rate Models 45 4.4 The Hull-White Model in More Detail 46 4.5 Trinomial Trees 47 5 Upwinding Techniques for Short Rate Models 55 5.1 Derivation of a PDE for Short Rate Models 55 5.2 Upwind Schemes 56 5.2.1 Model Equation 57 5.3 A Puttable Fixed Rate Bond under the Hull-White One Factor Model 63 5.3.1 Bond Details 64 5.3.2 Model Details 64 5.3.3 Numerical Method 65 5.3.4 An Algorithm in Pseudocode 68 5.3.5 Results 69 6 Boundary, Terminal and Interface Conditions and their Influence 71 6.1 Terminal Conditions for Equity Options 71 6.2 Terminal Conditions for Fixed Income Instruments 72 6.3 Callability and Bermudan Options 74 6.4 Dividends 74 6.5 Snowballs and TARNs 75 6.6 Boundary Conditions 77 6.6.1 Double Barrier Options and Dirichlet Boundary Conditions 77 6.6.2 Artificial Boundary Conditions and the Neumann Case 78 7 Finite Element Methods 81 7.1 Introduction 81 7.1.1 Weighted Residual Methods 81 7.1.2 Basic Steps 82 7.2 Grid Generation 83 7.3 Elements 85 7.3.1 1D Elements 86 7.3.2 2D Elements 88 7.4 The Assembling Process 90 7.4.1 Element Matrices 93 7.4.2 Time Discretization 97 7.4.3 Global Matrices 98 7.4.4 Boundary Conditions 101 7.4.5 Application of the Finite Element Method to Convection-Diffusion-Reaction Problems 103 7.5 A Zero Coupon Bond Under the Two Factor Hull-White Model 105 7.6 Appendix: Higher Order Elements 107 7.6.1 3D Elements 109 7.6.2 Local and Natural Coordinates 111 8 Solving Systems of Linear Equations 117 8.1 Direct Methods 118 8.1.1 Gaussian Elimination 118 8.1.2 Thomas Algorithm 119 8.1.3 LU Decomposition 120 8.1.4 Cholesky Decomposition 121 8.2 Iterative Solvers 122 8.2.1 Matrix Decomposition 123 8.2.2 Krylov Methods 125 8.2.3 Multigrid Solvers 126 8.2.4 Preconditioning 129 9 Monte Carlo Simulation 133 9.1 The Principles of Monte Carlo Integration 133 9.2 Pricing Derivatives with Monte Carlo Methods 134 9.2.1 Discretizing the Stochastic Differential Equation 135 9.2.2 Pricing Formalism 137 9.2.3 Valuation of a Steepener under a Two Factor Hull-White Model 137 9.3 An Introduction to the Libor Market Model 139 9.4 Random Number Generation 146 9.4.1 Properties of a Random Number Generator 147 9.4.2 Uniform Variates 148 9.4.3 Random Vectors 150 9.4.4 Recent Developments in Random Number Generation 151 9.4.5 Transforming Variables 152 9.4.6 Random Number Generation for Commonly Used Distributions 155 10 Advanced Monte Carlo Techniques 161 10.1 Variance Reduction Techniques 161 10.1.1 Antithetic Variates 161 10.1.2 Control Variates 163 10.1.3 Conditioning 166 10.1.4 Additional Techniques for Variance Reduction 168 10.2 Quasi Monte Carlo Method 169 10.2.1 Low-Discrepancy Sequences 169 10.2.2 Randomizing QMC 174 10.3 Brownian Bridge Technique 175 10.3.1 A Steepener under a Libor Market Model 177 11 Valuation of Financial Instruments with Embedded American/Bermudan Options within Monte Carlo Frameworks 179 11.1 Pricing American options using the Longstaff and Schwartz algorithm 179 11.2 A Modified Least Squares Monte Carlo Algorithm for Bermudan Callable Interest Rate Instruments 181 11.2.1 Algorithm: Extended LSMC Method for Bermudan Options 182 11.2.2 Notes on Basis Functions and Regression 185 11.3 Examples 186 11.3.1 A Bermudan Callable Floater under Different Short-rate Models 186 11.3.2 A Bermudan Callable Steepener Swap under a Two Factor Hull-White Model 188 11.3.3 A Bermudan Callable Steepener Cross Currency Swap in a 3D IR/FX Model Framework 189 12 Characteristic Function Methods for Option Pricing 193 12.1 Equity Models 194 12.1.1 Heston Model 196 12.1.2 Jump Diffusion Models 198 12.1.3 Infinite Activity Models 199 12.1.4 Bates Model 200 12.2 Fourier Techniques 201 12.2.1 Fast Fourier Transform Methods 201 12.2.2 Fourier-Cosine Expansion Methods 203 13 Numerical Methods for the Solution of PIDEs 209 13.1 A PIDE for Jump Models 209 13.2 Numerical Solution of the PIDE 210 13.2.1 Discretization of the Spatial Domain 211 13.2.2 Discretization of the Time Domain 211 13.2.3 A European Option under the Kou Jump Diffusion Model 212 13.3 Appendix: Numerical Integration via Newton-Cotes Formulae 214 14 Copulas and the Pitfalls of Correlation 217 14.1 Correlation 218 14.1.1 Pearson’s ρ 218 14.1.2 Spearman’s ρ 218 14.1.3 Kendall’s τ 220 14.1.4 Other Measures 221 14.2 Copulas 221 14.2.1 Basic Concepts 222 14.2.2 Important Copula Functions 222 14.2.3 Parameter estimation and sampling 229 14.2.4 Default Probabilities for Credit Derivatives 234 15 Parameter Calibration and Inverse Problems 239 15.1 Implied Black-Scholes Volatilities 239 15.2 Calibration Problems for Yield Curves 240 15.3 Reversion Speed and Volatility 245 15.4 Local Volatility 245 15.4.1 Dupire’s Inversion Formula 246 15.4.2 Identifying Local Volatility 246 15.4.3 Results 247 15.5 Identifying Parameters in Volatility Models 248 15.5.1 Model Calibration for the FTSE- 100 249 16 Optimization Techniques 253 16.1 Model Calibration and Optimization 255 16.1.1 Gradient-Based Algorithms for Nonlinear Least Squares Problems 256 16.2 Heuristically Inspired Algorithms 258 16.2.1 Simulated Annealing 259 16.2.2 Differential Evolution 260 16.3 A Hybrid Algorithm for Heston Model Calibration 261 16.4 Portfolio Optimization 265 17 Risk Management 269 17.1 Value at Risk and Expected Shortfall 269 17.1.1 Parametric VaR 270 17.1.2 Historical VaR 272 17.1.3 Monte Carlo VaR 273 17.1.4 Individual and Contribution VaR 274 17.2 Principal Component Analysis 276 17.2.1 Principal Component Analysis for Non-scalar Risk Factors 276 17.2.2 Principal Components for Fast Valuation 277 17.3 Extreme Value Theory 278 18 Quantitative Finance on Parallel Architectures 285 18.1 A Short Introduction to Parallel Computing 285 18.2 Different Levels of Parallelization 288 18.3 GPU Programming 288 18.3.1 CUDA and OpenCL 289 18.3.2 Memory 289 18.4 Parallelization of Single Instrument Valuations using (Q)MC 290 18.5 Parallelization of Hybrid Calibration Algorithms 291 18.5.1 Implementation Details 292 18.5.2 Results 295 19 Building Large Software Systems for the Financial Industry 297 Bibliography 301 Index 307

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Book SynopsisThis book presents an introduction to structural equation modeling (SEM) and facilitates the access of students and researchers in various scientific fields to this powerful statistical tool. It offers a didactic initiation to SEM as well as to the open-source software, lavaan, and the rich and comprehensive technical features it offers. Structural Equation Modeling with lavaan thus helps the reader to gain autonomy in the use of SEM to test path models and dyadic models, perform confirmatory factor analyses and estimate more complex models such as general structural models with latent variables and latent growth models. SEM is approached both from the point of view of its process (i.e. the different stages of its use) and from the point of view of its product (i.e. the results it generates and their reading). Table of ContentsPreface ix Introduction xi Chapter 1 Structural Equation Modeling 1 1.1 Basic concepts 2 1.1.1 Covariance and bivariate correlation 2 1.1.2 Partial correlation 5 1.1.3 Linear regression analysis 7 1.1.4 Standard error of the estimate 10 1.1.5 Factor analysis 11 1.1.6 Data distribution normality 18 1.2 Basic principles of SEM 21 1.2.1 Estimation methods (estimators) 27 1.3 Model evaluation of the solution of the estimated model 36 1.3.1 Overall goodness-of-fit indices 36 1.3.2 Local fit indices (parameter estimates) 43 1.3.3 Modification indices 44 1.4 Confirmatory approach in SEM 45 1.5 Basic conventions of SEM 47 1.6 Place and status of variables in a hypothetical model 49 1.7 Conclusion 49 1.8 Further reading 50 Chapter 2 Structural Equation Modeling Software 53 2.1 R environment 54 2.1.1 Installing R software 55 2.1.2 R console 55 2.2 lavaan 58 2.2.1 Installing the lavaan package 58 2.2.2 Launching lavaan 58 2.3 Preparing and importing a dataset 60 2.3.1 Entry and import of raw data 60 2.3.2 What to do in the absence of raw data? 63 2.4 Major operators of lavaan syntax 65 2.5 Main steps in using lavaan 66 2.6 lavaan fitting functions 68 Chapter 3 Steps in Structural Equation Modeling 69 3.1 The theoretical model and its conceptual specification 70 3.2 Model parameters and model identification 71 3.3 Models with observed variables (path models) 73 3.3.1 Identification of a path model 74 3.3.2 Model specification using lavaan (step 2) 76 3.3.3 Direct and indirect effects 78 3.3.4 The statistical significance of indirect effects 80 3.3.5 Model estimation with lavaan (step 3) 81 3.3.6 Model evaluation (step 4) 82 3.3.7 Recursive and non-recursive models 83 3.3.8 Illustration of a path analysis model 85 3.4 Actor-partner interdependence model 90 3.4.1 Specifying and estimating an APIM with lavaan 92 3.4.2 Evaluation of the solution 93 3.4.3 Evaluating the APIM re-specified with equality constraints 94 3.5 Models with latent variables (measurement models and structural models) 95 3.5.1 The measurement model or Confirmatory Factor Analysis 97 3.6 Hybrid models 148 3.7 Measure with a single-item indicator 149 3.8 General structural model including single-item latent variables with a single indicator 151 3.9 Conclusion 152 3.10 Further reading 155 Chapter 4 Advanced Topics: Principles and Applications 157 4.1 Multigroup analysis 157 4.1.1 The steps of MG-CFA 162 4.1.2 Model solutions and model comparison tests 166 4.1.3 Total invariance versus partial invariance 171 4.1.4 Specification of a partial invariance in lavaan syntax 172 4.2 Latent trait-state models 172 4.2.1 The STARTS model 173 4.2.2 The Trait-State-Occasion Model 197 4.2.3 Concluding remarks 211 4.3 Latent growth models 213 4.3.1 General overview 213 4.3.2 Illustration of an univariate linear growth model 223 4.3.3 Illustration of an univariate non-linear (quadratic) latent growth model 228 4.3.4 Conditional latent growth model 232 4.3.5 Second-order latent growth model 240 4.4 Further reading 249 References 251 Index 269

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Book SynopsisData science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences. Table of ContentsPreface xi Part 1. Symbolic Data 1 Chapter 1. Explanatory Tools for Machine Learning in the Symbolic Data Analysis Framework 3Edwin DIDAY 1.1. Introduction 4 1.2. Introduction to Symbolic Data Analysis 6 1.2.1. What are complex data? 6 1.2.2. What are “classes” and “class of complex data”? 7 1.2.3. Which kind of class variability? 7 1.2.4. What are “symbolic variables” and “symbolic data tables”? 7 1.2.5. Symbolic Data Analysis (SDA) 9 1.3. Symbolic data tables from Dynamic Clustering Method and EM 10 1.3.1. The “dynamical clustering method” (DCM) 10 1.3.2. Examples of DCM applications 10 1.3.3. Clustering methods by mixture decomposition 12 1.3.4. Symbolic data tables from clustering 13 1.3.5. A general way to compare results of clustering methods by the “explanatory power” of their associated symbolic data table 15 1.3.6. Quality criteria of classes and variables based on the cells of the symbolic data table containing intervals or inferred distributions 15 1.4. Criteria for ranking individuals, classes and their bar chart descriptive symbolic variables 16 1.4.1. A theoretical framework for SDA 16 1.4.2. Characterization of a category and a class by a measure of discordance 18 1.4.3. Link between a characterization by the criteria W and the standard Tf-Idf 19 1.4.4. Ranking the individuals, the symbolic variables and the classes of a bar chart symbolic data table 21 1.5. Two directions of research 23 1.5.1. Parametrization of concordance and discordance criteria 23 1.5.2. Improving the explanatory power of any machine learning tool by a filtering process 25 1.6. Conclusion 27 1.7. References 28 Chapter 2. Likelihood in the Symbolic Context 31Richard EMILION and Edwin DIDAY 2.1. Introduction 31 2.2. Probabilistic setting 32 2.2.1. Description variable and class variable 32 2.2.2. Conditional distributions 33 2.2.3. Symbolic variables 33 2.2.4. Examples 35 2.2.5. Probability measures on (ℂ, C), likelihood 37 2.3. Parametric models for p = 1 38 2.3.1. LDA model 38 2.3.2. BLS method 41 2.3.3. Interval-valued variables 42 2.3.4. Probability vectors and histogram-valued variables 42 2.4. Nonparametric estimation for p = 1 45 2.4.1. Multihistograms and multivariate polygons 45 2.4.2. Dirichlet kernel mixtures 45 2.4.3. Dirichlet Process Mixture (DPM) 45 2.5. Density models for p ≥ 2 46 2.6. Conclusion 46 2.7. References 47 Chapter 3. Dimension Reduction and Visualization of Symbolic Interval-Valued Data Using Sliced Inverse Regression 49Han-Ming WU, Chiun-How KAO and Chun-houh CHEN 3.1. Introduction 49 3.2. PCA for interval-valued data and the sliced inverse regression 51 3.2.1. PCA for interval-valued data 51 3.2.2. Classic SIR 52 3.3. SIR for interval-valued data 53 3.3.1. Quantification approaches 54 3.3.2. Distributional approaches 56 3.4. Projections and visualization in DR subspace 58 3.4.1. Linear combinations of intervals 58 3.4.2. The graphical representation of the projected intervals in the 2D DR subspace 59 3.5. Some computational issues 61 3.5.1. Standardization of interval-valued data 61 3.5.2. The slicing schemes for iSIR 62 3.5.3. The evaluation of DR components 62 3.6. Simulation studies 63 3.6.1. Scenario 1: aggregated data 63 3.6.2. Scenario 2: data based on interval arithmetic 63 3.6.3. Results 64 3.7. A real data example: face recognition data 65 3.8. Conclusion and discussion 73 3.9. References 74 Chapter 4. On the “Complexity” of Social Reality. Some Reflections About the Use of Symbolic Data Analysis in Social Sciences 79Frédéric LEBARON 4.1. Introduction 79 4.2. Social sciences facing “complexity” 80 4.2.1. The total social fact, a designation of “complexity” in social sciences 80 4.2.2. Two families of answers 80 4.2.3. The contemporary deepening of the two approaches, “reductionist” and “encompassing” 81 4.2.4. Issues of scale and heterogeneity 82 4.3. Symbolic data analysis in the social sciences: an example 83 4.3.1. Symbolic data analysis 83 4.3.2. An exploratory case study on European data 83 4.3.3. A sociological interpretation 94 4.4. Conclusion 95 4.5. References 96 Part 2. Complex Data 99 Chapter 5. A Spatial Dependence Measure and Prediction of Georeferenced Data Streams Summarized by Histograms 101Rosanna VERDE and Antonio BALZANELLA 5.1. Introduction 101 5.2. Processing setup 103 5.3. Main definitions 104 5.4. Online summarization of a data stream through CluStream for Histogram data 106 5.5. Spatial dependence monitoring: a variogram for histogram data 107 5.6. Ordinary kriging for histogram data 110 5.7. Experimental results on real data 112 5.8. Conclusion 116 5.9. References 116 Chapter 6. Incremental Calculation Framework for Complex Data 119Huiwen WANG, Yuan WEI and Siyang WANG 6.1. Introduction 119 6.2. Basic data 122 6.2.1. The basic data space 122 6.2.2. Sample covariance matrix 123 6.3. Incremental calculation of complex data 124 6.3.1. Transformation of complex data 124 6.3.2. Online decomposition of covariance matrix 125 6.3.3. Adopted algorithms 128 6.4. Simulation studies 131 6.4.1. Functional linear regression 131 6.4.2. Compositional PCA 133 6.5. Conclusion 135 6.6. Acknowledgment 135 6.7. References 135 Part 3. Network Data 139 Chapter 7. Recommender Systems and Attributed Networks 141Françoise FOGELMAN-SOULIÉ, Lanxiang MEI, Jianyu ZHANG, Yiming LI, Wen GE, Yinglan LI and Qiaofei YE 7.1. Introduction 141 7.2. Recommender systems 142 7.2.1. Data used 143 7.2.2. Model-based collaborative filtering 145 7.2.3. Neighborhood-based collaborative filtering 145 7.2.4. Hybrid models 148 7.3. Social networks 150 7.3.1. Non-independence 150 7.3.2. Definition of a social network 150 7.3.3. Properties of social networks 151 7.3.4. Bipartite networks 152 7.3.5. Multilayer networks 153 7.4. Using social networks for recommendation 154 7.4.1. Social filtering 154 7.4.2. Extension to use attributes 155 7.4.3. Remarks 156 7.5. Experiments 156 7.5.1. Performance evaluation 156 7.5.2. Datasets 157 7.5.3. Analysis of one-mode projected networks 158 7.5.4. Models evaluated 160 7.5.5. Results 160 7.6. Perspectives 163 7.7. References 163 Chapter 8. Attributed Networks Partitioning Based on Modularity Optimization 169David COMBE, Christine LARGERON, Baptiste JEUDY, Françoise FOGELMAN-SOULIÉ and Jing WANG 8.1. Introduction 169 8.2. Related work 171 8.3. Inertia based modularity 172 8.4. I-Louvain 174 8.5. Incremental computation of the modularity gain 176 8.6. Evaluation of I-Louvain method 179 8.6.1. Performance of I-Louvain on artificial datasets 179 8.6.2. Run-time of I-Louvain 180 8.7. Conclusion 181 8.8. References 182 Part 4. Clustering 187 Chapter 9. A Novel Clustering Method with Automatic Weighting of Tables and Variables 189Rodrigo C. DE ARAÚJO, Francisco DE ASSIS TENORIO DE CARVALHO and Yves LECHEVALLIER 9.1. Introduction 189 9.2. Related Work 190 9.3. Definitions, notations and objective 191 9.3.1. Choice of distances 192 9.3.2. Criterion W measures the homogeneity of the partition P on the set of tables 193 9.3.3. Optimization of the criterion W 195 9.4. Hard clustering with automated weighting of tables and variables 196 9.4.1. Clustering algorithms MND–W and MND–WT 196 9.5. Applications: UCI data sets 201 9.5.1. Application I: Iris plant 201 9.5.2. Application II: multi-features dataset 204 9.6. Conclusion 206 9.7. References 206 Chapter 10. Clustering and Generalized ANOVA for Symbolic Data Constructed from Open Data 209Simona KORENJAK-ČERNE, Nataša KEJŽAR and Vladimir BATAGELJ 10.1. Introduction 209 10.2. Data description based on discrete (membership) distributions 210 10.3. Clustering 212 10.3.1. TIMSS – study of teaching approaches 215 10.3.2. Clustering countries based on age–sex distributions of their populations 217 10.4. Generalized ANOVA 221 10.5. Conclusion 225 10.6. References 226 List of Authors 229 Index 233

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Book SynopsisAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.Table of ContentsIntroduction.- Statistical Learning.- Linear Regression.- Classification.- Resampling Methods.- Linear Model Selection and Regularization.- Moving Beyond Linearity.- Tree-Based Methods.- Support Vector Machines.- Deep Learning.- Survival Analysis and Censored data.- Unsupervised Learning.- Multiple Testing.- Index.

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Book SynopsisCombinatorica, an extension to the popular computer algebra system Mathematica®, is the most comprehensive software available for teaching and research applications of discrete mathematics. This definitive reference/user's guide provides examples of all 450 Combinatorica functions in action, along with tutorial text on the mathematical and algorithmic theory.Trade ReviewReview of the hardback: 'This book is the definite reference guide to Combinatorica … it is more than just a reference since it has all the necessary theory to comprehend the concepts … It is a very readable edition full of graphical and stimulating approaches to combinatorics and graph theories … This is a great resource for the acknowledgment of beautiful patterns and important properties of graphs and other combinatorial objects … This book is highly recommended. it is well organized, and readable textbook for beginners and intermediate students.' Leonardo On-lineTable of Contents1. Combinatorica: an explorer's guide; 2. Permutations and combinations; 3. Algebraic combinatorics; 4. Partitions, compositions and Young tableaux; 5. Graph representation; 6. Generating graphs; 7. Properties of graphs; 8. Algorithmic graph theory.

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Book SynopsisThis concise text, first published in 2003, is for a one-semester course for upper-level undergraduates and beginning graduate students in engineering, science, and mathematics, and can also serve as a quick reference for professionals. The treatment of each method is brief and technical issues are minimized, but all the issues important in practice and for understanding the code are discussed.Trade Review' … this is a readable, accessible text full of invaluable advice, illustrated using interesting examples and exercises … if you do have some background knowledge of numerical analysis, MATLAB, and are motivated by the application of numerical methods to real problems, you will find this book full of interest … the book acts as a useful introduction to several important, more general, issues in scientific computing.' The Mathematical GazetteTable of Contents1. Getting started; 2. Initial value problems; 3. Boundary value problems; 4. Delay differential equations.

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Book SynopsisThe first MATLAB-based numerical methods textbook specifically for bioengineers, including topics on hypothesis testing, plus numerous examples drawn exclusively from biomedical engineering applications. This is an ideal core text for one-semester undergraduate courses, and is also a valuable reference for anyone interested in the quantitative aspects of biology research.Trade Review'I think this book is a winner … [it] is really easy to read and places frameworks for numerical analysis into realistic bioengineering concepts that students will find familiar and relevant. This is most evident in the excellent boxed examples, but also in many of the homework problems. I also really liked the 'key points to consider' at the end of the chapters - these are useful reminders for the students. Finally, the book presents bioinformatics in a manageable fashion that should help demystify this subject for interested students.' K. Jane Grande-Allen, Rice UniversityTable of Contents1. Types and sources of numerical error; 2. Systems of linear equations; 3. Statistics and probability; 4. Hypothesis testing; 5. Root finding techniques for nonlinear equations; 6. Numerical quadrature; 7. Numerical integration of ordinary differential equations; 8. Nonlinear data regression and optimization; 9. Basic algorithms of bioinformatics; Appendix A. Introduction to MATLAB; Appendix B. Location of nodes for Gauss-Legendre quadrature.

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Book SynopsisWritten in a clear and lively tone, Statistics Using IBM SPSS provides a data-centric approach to statistics with integrated SPSS (version 22) commands, ensuring that students gain both a deep conceptual understanding of statistics and practical facility with the leading statistical software package. With one hundred worked examples, the textbook guides students through statistical practice using real data and avoids complicated mathematics. Numerous end-of-chapter exercises allow students to apply and test their understanding of chapter topics, with detailed answers available online. The third edition has been updated throughout and includes a new chapter on research design, new topics (including weighted mean, resampling with the bootstrap, the role of the syntax file in workflow management, and regression to the mean) and new examples and exercises. Student learning is supported by a rich suite of online resources, including answers to end-of-chapter exercises, real data sets, PowerTrade Review'This is the third edition of a very popular and useful text. The focus is on using SPSS in the research process. The chapters have illustrative exercises and meaningful real data problem sets that not only make it convenient for teaching but also provide realistic experiences for students that will stay with them for many years. The book does a very good job presenting the challenge of data analysis and the experience of being a serious researcher looking at important problems; it illustrates how a variety of quantitative methods can be applied to real data to tease out and evaluate the inferences suggested by that data. I strongly recommend this book to instructors of a one- or two-semester introductory statistics course.' Robert W. Lissitz, University of Maryland'This text by Weinberg and Abramowitz is an excellent choice for an undergraduate or introductory graduate course for non-majors. Stressing concepts over computation, it focuses on essential material for students in education and the social sciences. The book reads easily, like a set of well-constructed lectures that begin with simple fundamental concepts. Yet modern and relatively advanced topics, such as uses of the bootstrap, are also treated. Rather than focusing on hand calculations, the book integrates instruction on using SPSS directly into the text. This enables student exploration of actual research data sets, beginning in the first chapters.' James E. Corter, Columbia University'This book covers a broad range of topics in introductory statistics, employing a hands-on, problem-based approach. The latest edition expands an already long list of topics to include bootstrap techniques and experimental design considerations. By providing detailed, worked-through examples based on real data and substantive research questions, the authors guide the student through the data analysis process from beginning to end. However, this is no 'cookbook' - each section builds on the concepts and techniques established previously, and the reader is encouraged to explore the nuances involved in effective statistical analysis. What is particularly unique about the authors' exposition is that it can be read on many levels; this book will serve well as a course textbook or as a handy reference for the applied researcher.' Marc A. Scott, New York UniversityTable of Contents1. Introduction; 2. Examining univariate distributions; 3. Measures of location, spread, and skewness; 4. Re-expressing variables; 5. Exploring relationships between two variables; 6. Simple linear regression; 7. Probability fundamentals; 8. Theoretical probability models; 9. The role of sampling in inferential statistics; 10. Inferences involving the mean of a single population when σ is known; 11. Inferences involving the mean when σ is not known: one- and two-sample designs; 12. Research design: introduction and overview; 13. One-way analysis of variance; 14. Two-way analysis of variance; 15. Correlation and simple regression as inferential techniques; 16. An introduction to multiple regression; 17. Nonparametric methods.

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Book SynopsisSAS programming is a creative and iterative process designed to empower you to make the most of your organization''s data. This friendly guide provides you with a repertoire of essential SAS tools for data management, whether you are a new or an infrequent user. Most useful to students and programmers with little or no SAS experience, it takes a no-frills, hands-on tutorial approach to getting started with the software. You will find immediate guidance in navigating, exploring, visualizing, cleaning, formatting, and reporting on data using SAS and JMP. Step-by-step demonstrations, screenshots, handy tips, and practical exercises with solutions equip you to explore, interpret, process and summarize data independently, efficiently and effectively.Trade Review'The authors have created a very readable and gentle introduction to SAS programming and its working environment - Enterprise Guide. The text provides a valuable overview of 'navigating' in a SAS windowing environment and before moving quickly into core procedures. … a very valuable introduction to basic SAS programming for the beginning data analyst.' Glenn Gamst, University of la Verne, California'The authors of Data Management Essentials Using SAS and JMP have written a thoroughly user-friendly beginning guide to SAS programming for data exploration and management, and for designing well-constructed and informative reports. I honestly know of no better book to use for self-instruction on, or for teaching essential SAS programming skills for report design than this very well written and produced book.' Joseph M. Hilbe, Arizona State UniversityTable of Contents1. Navigation; 2. Preliminary data exploration; 3. Storing and manipulating data; 4. Advanced concepts in dataset and variable manipulation; 5. Introduction to common procedures; 6. Procedures for simple statistics; 7. More about common procedures; 8. Data visualization; 9. JMP as an alternative.

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Book SynopsisNow in its third edition, this outstanding textbook explains everything you need to get started using MATLAB . It contains concise explanations of essential MATLAB commands, as well as easily understood instructions for using MATLAB's programming features, graphical capabilities, simulation models, and rich desktop interface. MATLAB 8 and its new user interface is treated extensively in the book. New features in this edition include: a complete treatment of MATLAB's publish feature; new material on MATLAB graphics, enabling the user to master quickly the various symbolic and numerical plotting routines; and a robust presentation of MuPAD and how to use it as a stand-alone platform. The authors have also updated the text throughout, reworking examples and exploring new applications. The book is essential reading for beginners, occasional users and experienced users wishing to brush up their skills. Further resources are available from the authors' website at www-math.umd.edu/schol/a-guTrade ReviewReview of previous edition: 'Major highlights of the book are completely transparent examples of classical yet always intriguing mathematical, statistical, engineering, economics, and physics problems. In addition, the book explains a seamless use with Microsoft Word for integrating MATLAB® outputs with documents, reports, presentations, or other online processes. Advanced topics with examples include: Monte Carlo simulation, population dynamics, and linear programming. … an outstanding textbook, and, likewise, should be an integral part of the technical reference shelf for most IT professionals. It is a great resource for wherever MATLAB® is available!' ACM UbiquityReview of previous edition: 'This is a short, focused introduction to MATLAB®, a comprehensive software system for mathematical and technical computing. For the beginner it explains everything needed to start using MATLAB®, while experienced users ... will find much useful information here.' L'enseignement mathematiqueTable of ContentsPreface; 1. Getting started; 2. MATLAB basics; 3. Interacting with MATLAB; Practice Set A. Algebra and arithmetic; 4. Beyond the basics; 5. MATLAB graphics; 6. MATLAB programming; 7. Publishing and M-books; Practice Set B. Math, graphics, and programming; 8. MuPAD; 9. Simulink; 10. GUIs; 11. Applications; Practice Set C. Developing your MATLAB skills; 12. Troubleshooting; Solutions to the practice sets; Glossary; Index.

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Book SynopsisAn SPSS Companion for the Third Edition of The Fundamentals of Political Science Research offers students a chance to delve into the world of SPSS using real political science data sets and statistical analysis techniques directly from Paul M. Kellstedt and Guy D. Whitten''s best-selling textbook. Built in parallel with the main text, this workbook teaches students to apply the techniques they learn in each chapter by reproducing the analyses and results from each lesson using SPSS. Students will also learn to create all of the tables and figures found in the textbook, leading to an even greater mastery of the core material. This accessible, informative, and engaging companion walks through the use of SPSS step-by-step, using command lines and screenshots to demonstrate proper use of the software. With the help of these guides, students will become comfortable creating, editing, and using data sets in SPSS to produce original statistical analyses for evaluating causal claims. End-of-chTable of Contents1. The scientific study of politics; 2. The art of theory building; 3. Evaluating causal relationships; 4. Research design; 5. Measuring concepts of interest; 6. Getting to know your data; 7. Probability and statistical inference; 8. Bivariate hypothesis testing; 9. Two-variable regression models; 10. Multiple regression: the basics; 11. Multiple regression model specification; 12. Limited dependent variables and time-series data; Bibliography; Index.

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Book SynopsisCUDA is now the dominant language used for programming GPUs, one of the most exciting hardware developments of recent decades. With CUDA, you can use a desktop PC for work that would have previously required a large cluster of PCs or access to a HPC facility. As a result, CUDA is increasingly important in scientific and technical computing across the whole STEM community, from medical physics and financial modelling to big data applications and beyond. This unique book on CUDA draws on the author''s passion for and long experience of developing and using computers to acquire and analyse scientific data. The result is an innovative text featuring a much richer set of examples than found in any other comparable book on GPU computing. Much attention has been paid to the C++ coding style, which is compact, elegant and efficient. A code base of examples and supporting material is available online, which readers can build on for their own projects--Table of Contents1. Introduction to GPU kernels and hardware; 2. Thinking and coding in parallel; 3. Warps and cooperative groups; 4. Parallel stencils; 5. Textures; 6. Monte Carlo applications; 7. Concurrency using CUDA streams and events; 8. Application to PET scanners; 9. Scaling up; 10. Tools for profiling and debugging; 11. Tensor cores; A. A brief history of CUDA; B. Atomic operations; C. The NVCC complier; D. AVX and the Intel complier; E. Number formats; F. CUDA documentation and libraries; G. The CX header files; H. AI and Python; I. Topics in C++; Index.

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Book SynopsisProviding a detailed and pedagogical account of the rapidly-growing field of computational statistical physics, this book covers both the theoretical foundations of equilibrium and non-equilibrium statistical physics, and also modern, computational applications such as percolation, random walks, magnetic systems, machine learning dynamics, and spreading processes on complex networks. A detailed discussion of molecular dynamics simulations is also included, a topic of great importance in biophysics and physical chemistry. The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at graduate level, and numerous worked examples and end of chapter problems allow students to test their progress and understanding.Table of ContentsPreface; Part I. Stochastic Methods: 1. Random Numbers; 2. Random-Geometrical Models; 3. Equilibrium Systems; 4. Monte-Carlo Methods; 5. Phase Transitions; 6. Cluster Algorithms; 7. Histogram Methods; 8. Renormalization Group; 9. Learning and Optimizing; 10. Parallelization; 11. Non-Equilibrium Systems; Part II. Molecular Dynamics: 12. Basic Molecular Dynamics; 13. Optimizing Molecular Dynamics; 14. Dynamics of Composed Particles; 15. Long-Range Potentials; 16. Canonical Ensemble; 17. Inelastic Collisions in Molecular Dynamics; 18. Event-Driven Molecular Dynamics; 19. Non-Spherical Particles; 20. Contact Dynamics; 21. Discrete Fluid Models; 22. Ab-Initio Simulations; References; Index.

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Book SynopsisMaple is a powerful symbolic computation system that is widely used in universities around the world. This short introduction gives readers an insight into the rules that control how the system works, and how to understand, fix, and avoid common problems. Topics covered include algebra, calculus, linear algebra, graphics, programming, and procedures. Each chapter contains numerous illustrative examples, using mathematics that does not extend beyond first-year undergraduate material. Maple worksheets containing these examples are available for download from the author''s personal website. The book is suitable for new users, but where advanced topics are central to understanding Maple they are tackled head-on. Many concepts which are absent from introductory books and manuals are described in detail. With this book, students, teachers and researchers will gain a solid understanding of Maple and how to use it to solve complex mathematical problems in a simple and efficient way.Trade Review'Thompson (Univ. of Liverpool, UK) clearly knows the 'gotchas' that most often plague beginners (and others!) and provides pointed guidance; extracting the same vital information buried in longer and more systematic treatises and manuals can certainly prove a challenge. Summing Up: Recommended. Lower-division undergraduates and above; faculty and professionals.' D. V. Feldman, CHOICETable of Contents1. Introduction; 2. Getting started; 3. Algebra and calculus; 4. Solving equations; 5. Linear algebra; 6. Graphics; 7. Programming; 8. Procedures; 9. Example programs; Appendix A. Other ways to run Maple; Appendix B. Terminating characters; Index of Maple notation.

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Book SynopsisDesign and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.Drawing on his many years of working in the pharmaceutical, agricultural, industrial chemicals, and machinery industries, the author teaches students how to: Make an appropriate design choice based on the objectives of a research project Create a design and perform an experiment Interpret the results of computer data analysis The book emphasizes the connection among the experimental units, the way treatments are randomized to experimental units, and the proper error term for data analysis. R code is uTrade Review"This is an excellent but demanding text. … This book should be mandatory reading for anyone teaching a course in the statistical design of experiments. … reading this text is likely to influence their course for the better."—MAA Reviews, March 2015"Thank you for writing your phenomenal book "Design and Analysis of Experiments with R". I'm teaching a new course this spring on experimental design and reinforcement learning. The students are graduate bioengineers, so I was having difficulty finding a text that blends theory, practice, and computation. Your book excels at all three. The first chapter I read clarified several topics and improved both my teaching and research. After testing a dozen DOE and RSM books, yours is the clear winner. I understand the enormous time that goes into a well-constructed textbook. I hope this message conveys my deep appreciation for your effort."—Paul Jensen, Ph.D., Assistant Professor , Department of Bioengineering and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign"This is an excellent but demanding text. … This book should be mandatory reading for anyone teaching a course in the statistical design of experiments. … reading this text is likely to influence their course for the better."—MAA Reviews, March 2015"In my opinion, this is a very valuable book. It covers the topics that I judge should be in such a book including what might be called the standard designs and more … it has become my go to text on experimental design."David E. Booth, TechnometricsTable of ContentsIntroduction. Completely Randomized Designs with One Factor. Factorial Designs. Randomized Block Designs. Designs to Study Variances. Fractional Factorial Designs. Incomplete and Confounded Block Designs. Split-Plot Designs. Crossover and Repeated Measures Designs. Response Surface Designs. Mixture Experiments. Robust Parameter Design Experiments. Experimental Strategies for Increasing Knowledge. Bibliography. Index.

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Book SynopsisWe have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments. Trade ReviewFrom the reviews: MATHEMATICAL REVIEWS "Although the book is written as a textbook, with many carefully worked out examples and exercises, it will be very useful for the researcher since the authors discuss their favorite research topics (Monte Carlo optimization and convergence diagnostics) going through many relevant references…This book is a comprehensive treatment of the subject and will be an essential reference for statisticians working with McMC." From the reviews of the second edition: "Only 2 years after its first edition this carefully revised second edition accounts for the rapid development in this field...This book can be highly recommended for students and researchers interested in learning more about MCMC methods and their background." Biometrics, March 2005 "This is a comprehensive book for advanced graduate study by statisticians." Technometrics, May 2005 "This excellent text is highly recommended..." Short Book Reviews of the ISI, April 2005 "This book provides a thorough introduction to Monte Carlo methods in statistics with an emphasis on Markov chain Monte Carlo methods. … Each chapter is concluded by problems and notes. … The book is self-contained and does not assume prior knowledge of simulation or Markov chains. …. on the whole it is a readable book with lots of useful information." (Søren Feodor Nielsen, Journal of Applied Statistics, Vol. 32 (6), August, 2005) "This revision of the influential 1999 text … includes changes to the presentation in the early chapters and much new material related to MCMC and Gibbs sampling. The result is a useful introduction to Monte Carlo methods and a convenient reference for much of current methodology. … The numerous problems include many with analytical components. The result is a very useful resource for anyone wanting to understand Monte Carlo procedures. This excellent text is highly recommended … ." (D.F. Andrews, Short Book Reviews, Vol. 25 (1), 2005) "You have to practice statistics on a desert island not to know that Markov chain Monte Carlo (MCMC) methods are hot. That situation has caused the authors not only to produce a new edition of their landmark book but also to completely revise and considerably expand it. … This is a comprehensive book for advanced graduate study by statisticians." (Technometrics, Vol. 47 (2), May, 2005) "This remarkable book presents a broad and deep coverage of the subject. … This second edition is a considerably enlarged version of the first. Some subjects that have matured more rapidly in the five years following the first edition, like reversible jump processes, sequential MC, two-stage Gibbs sampling and perfect sampling have now chapters of their own. … the book is also very well suited for self-study and is also a valuable reference for any statistician who wants to study and apply these techniques." (Ricardo Maronna, Statistical Papers, Vol. 48, 2006) "This second edition of ‘Monte Carlo Statistical Methods’ has appeared only five years after the first … the new edition aims to incorporate recent developments. … Each chapter includes sections with problems and notes. … The style of the presentation and many carefully designed examples make the book very readable and easily accessible. It represents a comprehensive account of the topic containing valuable material for lecture courses as well as for research in this area." (Evelyn Buckwar, Zentrablatt MATH, Vol. 1096 (22), 2006) "This is a useful and utilitarian book. It provides a catalogue of modern Monte carlo based computational techniques with ultimate emphasis on Markov chain Monte Carlo (MCMC) … . an excellent reference for anyone who is interested in algorithms for various modes of Markov chain (MC) methodology … . a must for any researcher who believes in the importance of understanding what goes on inside of the MCMC ‘black box.’ … I recommend the book to all who wish to learn about statistical simulation." (Wesley O. Johnson, Journal of the American Statistical Association, Vol. 104 (485), March, 2009)Table of ContentsIntroduction * Random Variable Generation * Monte Carlo Integration * Controlling Monte Carlo Variance * Monte Carlo Optimization * Markov Chains * The Metropolis-Hastings Algorithm * The Slice Sampler * The Two-Stage Gibbs Sampler * The Multi-Stage Gibbs Sampler * Variable Dimension Models and Reversible Jump * Diagnosing Convergence * Perfect Sampling * Iterated and Sequential Importance Sampling

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Trade ReviewFrom the reviews of the second edition: "This fascinating book deconstructs the process of producing graphics and in doing so raises many fascinating questions on the nature and representation of information...This second edition is almost twice the size of the original, with six new chapters and substantial revisions." Short Book Reviews of the International Statistical Institute, December 2005 "When the first edidtion of this book appeared in 2000 it was much praised. I called it a tour de force of the highest order. (Wainer, 2001), Edward Wegman (2000) argued that it was destined to become a classic. Now, six years later this very fine book has been much improved." Howard Wainer for Psychometrika "...The second edition is an impressive expansion beyond a quite remarkable first edition. The text remains dense and even more encyclopedic, but it is a pleasure to read, whether a novice or an expert in graphics...this book is a bargain...The second edition is a must-have volume for anyone interested in graphics." Thomas E. Bradstreet for the Journal of the American Statistical Association, December 2006 "I find myself still thinking about the book and its ideas, several weeks after I finished reading it. I love that kind of book." Mark Bailey for Techometrics, Vol. 49, No. 1, February 2007 "Warts and all, The Grammar of Graphics is a richly rewarding work, an outstanding achievement by one of the leaders of statistical graphics. Seek it out." Nicholas J. Cox for the Journal of Statistical Software, January 2007 "The second edition is a quite fascinating book as well, and it comes with many color graphics. Anyone working in this field can see how many hours the author (plus coworkers) has spent on such a volume. … Demands for good graphics are high and this book will help to wetten the appetite to create future computer packages that will meet this demand. An occasional reader will get insights into a modern world of computing … ." (Wolfgang Polasek, Statistical Papers, Vol. 48, 2007)Table of ContentsSyntax.- How To Make a Pie.- Data.- Variables.- Algebra.- Scales.- Statistics.- Geometry.- Coordinates.- Aesthetics.- Facets.- Guides.- Semantics.- Space.- Time.- Uncertainty.- Analysis.- Control.- Automation.- Reader.- Coda.

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Book Synopsisand the building of the Three “M’s” Maple, Mathematica and Matlab. We intend to persuade that Maple and other like tools are worth knowing assuming only that one wishes to be a mathematician, a mathematics educator, a computer scientist, an engineer or scientist, or anyone else who wishes/needs to use mathematics better.Trade ReviewFrom the reviews:“This book is intended to teach the reader the usage of the computer algebra system Maple. … The book is readable and valuable to mathematics, science, and engineering undergraduates at the sophomore or above level. It could also be valuable to practitioners in those fields who want to learn Maple in situ. … Summing Up: Recommended. Lower-division undergraduates through graduate students; professionals.” (D. Z. Spicer, Choice, Vol. 49 (5), January, 2012)“This is a Maple-application book which illustrates some basic areas of mathematics by symbolic computation examples. … The presentation is clear with all necessary details and comments for ensuring a full understanding of the considered examples. The intended beneficiaries are undergraduate students, teachers giving courses to undergraduate students, as well as programmers interested in using Maple for several classes of mathematical problems.” (Octavian Pastravanu, Zentralblatt MATH, Vol. 1228, 2012)“In An Introduction to Modern Mathematical Computing with Maple, Borwein and Skerritt show that computers are an excellent companion for learning mathematics. … The theme of the book is that Maple can supplement mathematics learning and, what is more, can do much of the mathematics for the students. … The temptation is tremendous for students to skip the real work to have a true understanding of mathematics.” (David S. Mazel, The Mathematical Association of America, June, 2012)Table of Contents-Preface. -Conventions and Notation.-1. Number Theory (Introduction to Maple, Putting it together, Enough code, already. Show me some maths!, Problems and Exercises, Further Explorations). -2. Calculus(Revision and Introduction, Univariate Calculus, Multivariate Calculus, Exercises, Further Explorations). -3. Linear Algebra (Introduction and Review, Vector Spaces, Linear Transformations, Exercises, Further Explorations). -4. Visualisation and Geometry: a postscript (Useful Visualisation Tools, Geometry and Geometric Constructions). –A. Sample Quizzes (Number Theory, Calculus, Linear Algebra). –Index. –References

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Book SynopsisThe techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.Trade ReviewFrom the book reviews:“The monograph concentrates on intelligent systems for decision support based on probabilistic models, including Bayesian networks and influence diagrams. … This monograph provides a review of recent state affairs of probabilistic networks that can be useful for professionals, practitioners, and researchers from diverse fields of statistics and related disciplines. I think it can be used as a textbook in its own right for an upper level undergraduate course, especially for a reading course.” (Technometrics, Vol. 55 (2), May, 2013)Table of ContentsIntroduction.- Networks.- Probabilities.- Probabilistic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data-Driven Modeling.- Conflict Analysis.- Sensitivity Analysis.- Value of Information Analysis.- Quick Reference to Model Construction.- List of Examples.- List of Figures.- List of Tables.- List of Symbols.- References.- Index.

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