Mathematical and statistical software Books

Book SynopsisOffers an introduction to the modern statistical methods for ecology. This book shows how to construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. It covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions.Trade Review"Bolker's book is a must-buy for anyone wanting to fit data to models and go beyond hypothesis testing, but it is certainly not an 'introductory' text in the sense of 'simple'. This book is a tour de force for anyone who studied ecology for his or her interest of nature's working. But it is the one single book that can propel the statistical novice to the cutting edge of statistical ecology--albeit with blood, sweat and tears."--Carsten F. Dormann, Basic and Applied Ecology "[A] must for natural scientists and for statisticians who are interested in ecological applications... Numerous enlightening footnotes, meaningful graphics and direct speech are evidence of substantial classroom experience of the author... The book addresses students and researchers who have or have had some basic knowledge in ecology, mathematics and statistics. Delivering many examples and profound details on numerical aspects of maximum likelihood estimation, the book may also give a red line for a course in computational statistics."--Martin Schlather, Biometrical Journal "[T]his book succeeds both in explaining how to analyze many types of ecological data, and in clearly describing the theoretical background behind some common analyses and approaches. I expect to refer to it often."--Lynda D. Prior, Austral EcologyTable of ContentsAcknowledgments ix Chapter 1: Introduction and Background 1 1.1 Introduction 1 1.2 What This Book Is Not About 3 1.3 Frameworks for Modeling 5 1.4 Frameworks for Statistical Inference 10 1.5 Frameworks for Computing 17 1.6 Outline of the Modeling Process 20 1.7 R Supplement 22 Chapter 2: Exploratory Data Analysis and Graphics 29 2.1 Introduction 29 2.2 Getting Data into R 30 2.3 Data Types 34 2.4 Exploratory Data Analysis and Graphics 40 2.5 Conclusion 59 2.6 R Supplement 59 Chapter 3: Deterministic Functions for Ecological Modeling 72 3.1 Introduction 72 3.2 Finding Out about Functions Numerically 73 3.3 Finding Out about Functions Analytically 76 3.4 Bestiary of Functions 87 3.5 Conclusion 100 3.6 R Supplement 100 Chapter 4: Probability and Stochastic Distributions for Ecological Modeling 103 4.1 Introduction: Why Does Variability Matter? 103 4.2 Basic Probability Theory 104 4.3 Bayes' Rule 107 4.4 Analyzing Probability Distributions 115 4.5 Bestiary of Distributions 120 4.6 Extending Simple Distributions: Compounding and Generalizing 137 4.7 R Supplement 141 Chapter 5: Stochastic Simulation and Power Analysis 147 5.1 Introduction 147 5.2 Stochastic Simulation 148 5.3 Power Analysis 156 Chapter 6: Likelihood and All That 169 6.1 Introduction 169 6.2 Parameter Estimation: Single Distributions 169 6.3 Estimation for More Complex Functions 182 6.4 Likelihood Surfaces, Profiles, and Confidence Intervals 187 6.5 Confidence Intervals for Complex Models: Quadratic Approximation 196 6.6 Comparing Models 201 6.7 Conclusion 220 Chapter 7: Optimization and All That 222 7.1 Introduction 222 7.2 Fitting Methods 223 7.3 Markov Chain Monte Carlo 233 7.4 Fitting Challenges 241 7.5 Estimating Confidence Limits of Functions of Parameters 250 7.6 R Supplement 258 Chapter 8: Likelihood Examples 263 8.1 Tadpole Predation 263 8.2 Goby Survival 276 8.3 Seed Removal 283 Chapter 9: Standard Statistics Revisited 298 9.1 Introduction 298 9.2 General Linear Models 300 9.3 Nonlinearity: Nonlinear Least Squares 306 9.4 Nonnormal Errors: Generalized Linear Models 308 9.5 R Supplement 312 Chapter 10: Modeling Variance 316 10.1 Introduction 316 10.2 Changing Variance within Blocks 318 10.3 Correlations: Time-Series and Spatial Data 320 10.4 Multilevel Models: Special Cases 324 10.5 General Multilevel Models 327 10.6 Challenges 333 10.7 Conclusion 334 10.8 R Supplement 335 Chapter 11: Dynamic Models 337 11.1 Introduction 337 11.2 Simulating Dynamic Models 338 11.3 Observation and Process Error 342 11.4 Process and Observation Error 344 11.5 SIMEX 346 11.6 State-Space Models 348 11.7 Conclusions 357 11.8 R Supplement 360 Chapter 12: Afterword 362 Appendix Algebra and Calculus Basics 363 A.1 Exponentials and Logarithms 363 A.2 Differential Calculus 364 A.3 Partial Differentiation 364 A.4 Integral Calculus 365 A.5 Factorials and the Gamma Function 365 A.6 Probability 365 A.7 The Delta Method 366 A.8 Linear Algebra Basics 366 Bibliography 369 Index of R Arguments, Functions, and Packages 383 General Index 389

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Book SynopsisTable of ContentsIntroduction 1 About This Book 2 What’s New in This Edition 2 What’s New in Excel (Microsoft 365) 3 Foolish Assumptions 3 Icons Used in This Book 4 Where to Go from Here 5 Beyond This Book 5 Part 1: Getting Started With Statistical Analysis With Excel: A Marriage Made In Heaven 7 Chapter 1: Evaluating Data in the Real World 9 The Statistical (and Related) Notions You Just Have to Know 9 Samples and populations 10 Variables: Dependent and independent 11 Types of data 12 A little probability 13 Inferential Statistics: Testing Hypotheses 14 Null and alternative hypotheses 15 Two types of error 16 Some Excel Fundamentals 18 Autofilling cells 22 Referencing cells 25 Chapter 2: Understanding Excel’s Statistical Capabilities 29 Getting Started 30 Setting Up for Statistics 32 Worksheet functions 32 Quickly accessing statistical functions 36 Array functions 38 What’s in a name? An array of possibilities 41 Creating Your Own Array Formulas 50 Using data analysis tools 51 Additional data analysis tool packages 56 Accessing Commonly Used Functions 58 The New Analyze Data Tool 59 Data from Pictures! 60 Part 2: Describing Data 63 Chapter 3: Show-and-Tell: Graphing Data 65 Why Use Graphs? 65 Examining Some Fundamentals 67 Gauging Excel’s Graphics (Chartics?) Capabilities 68 Becoming a Columnist 69 Stacking the Columns 73 Slicing the Pie 74 A word from the wise 76 Drawing the Line 77 Adding a Spark 80 Passing the Bar 82 The Plot Thickens 84 Finding Another Use for the Scatter Chart 88 Chapter 4: Finding Your Center 91 Means: The Lore of Averages 91 Calculating the mean 92 AVERAGE and AVERAGEA 93 AVERAGEIF and AVERAGEIFS 95 TRIMMEAN 99 Other means to an end 100 Medians: Caught in the Middle 102 Finding the median 102 MEDIAN 103 Statistics à la Mode 104 Finding the mode 104 MODE.SNGL and MODE.MULT 104 Chapter 5: Deviating from the Average 107 Measuring Variation 108 Averaging squared deviations: Variance and how to calculate it 108 VAR.P and VARPA 111 Sample variance 113 VAR.S and VARA 114 Back to the Roots: Standard Deviation 114 Population standard deviation 115 STDEV.P and STDEVPA 115 Sample standard deviation 116 STDEV.S and STDEVA 116 The missing functions: STDEVIF and STDEVIFS 117 Related Functions 121 DEVSQ 121 Average deviation 122 AVEDEV 123 Chapter 6: Meeting Standards and Standings 125 Catching Some Z’s 126 Characteristics of z-scores 126 Bonds versus the Bambino 127 Exam scores 128 STANDARDIZE 128 Where Do You Stand? 131 RANK.EQ and RANK.AVG 131 LARGE and SMALL 133 PERCENTILE.INC and PERCENTILE.EXC 134 PERCENTRANK.INC and PERCENTRANK.EXC 137 Data analysis tool: Rank and Percentile 138 Chapter 7: Summarizing It All 141 Counting Out 141 COUNT, COUNTA, COUNTBLANK, COUNTIF, COUNTIFS 141 The Long and Short of It 144 MAX, MAXA, MIN, and MINA 144 Getting Esoteric 145 SKEW and SKEW.P 146 KURT 148 Tuning In the Frequency 150 FREQUENCY 150 Data analysis tool: Histogram 152 Can You Give Me a Description? 154 Data analysis tool: Descriptive Statistics 154 Be Quick About It! 156 Instant Statistics 159 Chapter 8: What’s Normal? 161 Hitting the Curve 161 Digging deeper 162 Parameters of a normal distribution 163 NORM.DIST 165 NORM.INV 167 A Distinguished Member of the Family 168 NORM.S.DIST 169 NORM.S.INV 170 PHI and GAUSS 170 Graphing a Standard Normal Distribution 171 Part 3: Drawing Conclusions From Data 173 Chapter 9: The Confidence Game: Estimation 175 Understanding Sampling Distributions 176 An EXTREMELY Important Idea: The Central Limit Theorem 177 (Approximately) simulating the Central Limit Theorem 178 The Limits of Confidence 183 Finding confidence limits for a mean 183 CONFIDENCE.NORM 186 Fit to a t 187 CONFIDENCE.T 188 Chapter 10: One-Sample Hypothesis Testing 189 Hypotheses, Tests, and Errors 190 Hypothesis Tests and Sampling Distributions 191 Catching Some Z’s Again 193 Z.TEST 196 t for One 197 T.DIST, T.DIST.RT, and T.DIST.2T 198 T.INV and T.INV.2T 200 Visualizing a t-Distribution 201 Testing a Variance 203 CHISQ.DIST and CHISQ.DIST.RT 205 CHISQ.INV and CHISQ.INV.RT 206 Visualizing a Chi-Square Distribution 208 Chapter 11: Two-Sample Hypothesis Testing 211 Hypotheses Built for Two 211 Sampling Distributions Revisited 212 Applying the Central Limit Theorem 213 Z’s once more 215 Data analysis tool: z-Test: Two Sample for Means 216 t for Two 219 Like peas in a pod: Equal variances 220 Like p’s and q’s: Unequal variances 221 T.TEST 222 Data analysis tool: t-Test: Two Sample 223 A Matched Set: Hypothesis Testing for Paired Samples 227 T.TEST for matched samples 228 Data analysis tool: t-Test: Paired Two Sample for Means 230 t-tests on the iPad with StatPlus 232 Testing Two Variances 235 Using F in conjunction with t 237 F.TEST 238 F.DIST and F.DIST.RT 240 F.INV and F.INV.RT 241 Data analysis tool: F-test: Two Sample for Variances 242 Visualizing the F-Distribution 244 Chapter 12: Testing More Than Two Samples 247 Testing More than Two 247 A thorny problem 248 A solution 249 Meaningful relationships 253 After the F-test 254 Data analysis tool: Anova: Single Factor 258 Comparing the means 260 Another Kind of Hypothesis, Another Kind of Test 262 Working with repeated measures ANOVA 262 Getting trendy 264 Data analysis tool: Anova: Two-Factor Without Replication 268 Analyzing trend 271 ANOVA on the iPad 272 ANOVA on the iPad: Another Way 274 Repeated Measures ANOVA on the iPad 277 Chapter 13: Slightly More Complicated Testing 281 Cracking the Combinations 281 Breaking down the variances 282 Data analysis tool: Anova: Two-Factor Without Replication 284 Cracking the Combinations Again 286 Rows and columns 286 Interactions 287 The analysis 288 Data analysis tool: Anova: Two-Factor With Replication 289 Two Kinds of Variables — at Once 292 Using Excel with a Mixed Design 293 Graphing the Results 298 After the ANOVA 300 Two-Factor ANOVA on the iPad 300 Chapter 14: Regression: Linear and Multiple 303 The Plot of Scatter 303 Graphing a line 305 Regression: What a Line! 307 Using regression for forecasting 309 Variation around the regression line 309 Testing hypotheses about regression 311 Worksheet Functions for Regression 317 SLOPE, INTERCEPT, STEYX 318 FORECAST.LINEAR 319 Array function: TREND 319 Array function: LINEST 323 Data Analysis Tool: Regression 325 Working with tabled output 327 Opting for graphical output 329 Juggling Many Relationships at Once: Multiple Regression 330 Excel Tools for Multiple Regression 331 TREND revisited 331 LINEST revisited 333 Regression data analysis tool revisited 336 Regression Analysis on the iPad 338 Chapter 15: Correlation: The Rise and Fall of Relationships 341 Scatterplots Again 341 Understanding Correlation 342 Correlation and Regression 345 Testing Hypotheses about Correlation 347 Is a correlation coefficient greater than zero? 348 Do two correlation coefficients differ? 349 Worksheet Functions for Correlation 350 CORREL and PEARSON 350 RSQ 351 COVARIANCE.P and COVARIANCE.S 352 Data Analysis Tool: Correlation 353 Tabled output 354 Multiple correlation 355 Partial correlation 356 Semipartial correlation 357 Data Analysis Tool: Covariance 358 Using Excel to Test Hypotheses about Correlation 358 Worksheet functions: FISHER, FISHERINV 359 Correlation Analysis on the iPad 360 Chapter 16: It’s About Time 363 A Series and Its Components 363 A Moving Experience 364 Lining up the trend 365 Data analysis tool: Moving Average 365 How to Be a Smoothie, Exponentially 368 One-Click Forecasting 369 Working with Time Series on the iPad 374 Chapter 17: Nonparametric Statistics 379 Independent Samples 380 Two samples: Mann-Whitney U test 380 More than two samples: Kruskal-Wallis one-way ANOVA 382 Matched Samples 383 Two samples: Wilcoxon matched-pairs signed ranks 384 More than two samples: Friedman two-way ANOVA 386 More than two samples: Cochran’s Q 387 Correlation: Spearman’s rS 389 A Heads-Up 391 Part 4: Probability 393 Chapter 18: Introducing Probability 395 What Is Probability? 395 Experiments, trials, events, and sample spaces 396 Sample spaces and probability 396 Compound Events 397 Union and intersection 397 Intersection, again 398 Conditional Probability 399 Working with the probabilities 400 The foundation of hypothesis testing 400 Large Sample Spaces 400 Permutations 401 Combinations 402 Worksheet Functions 403 FACT 403 PERMUT and PERMUTIONA 403 COMBIN and COMBINA 404 Random Variables: Discrete and Continuous 405 Probability Distributions and Density Functions 405 The Binomial Distribution 407 Worksheet Functions 409 BINOM.DIST and BINOM.DIST.RANGE 409 NEGBINOM.DIST 411 Hypothesis Testing with the Binomial Distribution 412 BINOM.INV 413 More on hypothesis testing 414 The Hypergeometric Distribution 415 HYPGEOM.DIST 416 Chapter 19: More on Probability 419 Discovering Beta 419 BETA.DIST 421 BETA.INV 423 Poisson 424 POISSON.DIST 425 Working with Gamma 427 The gamma function and GAMMA 427 The gamma distribution and GAMMA.DIST 428 GAMMA.INV 430 Exponential 431 EXPON.DIST 431 Chapter 20: Using Probability: Modeling and Simulation 433 Modeling a Distribution 434 Plunging into the Poisson distribution 434 Visualizing the Poisson distribution 435 Working with the Poisson distribution 436 Using POISSON.DIST again 437 Testing the model’s fit 437 A word about CHISQ.TEST 440 Playing ball with a model 441 A Simulating Discussion 444 Taking a chance: The Monte Carlo method 444 Loading the dice 444 Data analysis tool: Random Number Generation 445 Simulating the Central limit Theorem 448 Simulating a business 452 Chapter 21: Estimating Probability: Logistic Regression 457 Working Your Way Through Logistic Regression 458 Mining with XLMiner 460 Part 5: The Part of Tens 465 Chapter 22: Ten (12, Actually) Statistical and Graphical Tips and Traps 467 Significant Doesn’t Always Mean Important 467 Trying to Not Reject a Null Hypothesis Has a Number of Implications 468 Regression Isn’t Always Linear 468 Extrapolating Beyond a Sample Scatterplot Is a Bad Idea 469 Examine the Variability Around a Regression Line 469 A Sample Can Be Too Large 470 Consumers: Know Your Axes 470 Graphing a Categorical Variable as a Quantitative Variable Is Just Plain Wrong 471 Whenever Appropriate, Include Variability in Your Graph 472 Be Careful When Relating Statistics Textbook Concepts to Excel 472 It’s Always a Good Idea to Use Named Ranges in Excel 472 Statistical Analysis with Excel on the iPad Is Pretty Good! 473 Chapter 23: Ten Topics (Thirteen, Actually) That Just Don’t Fit Elsewhere 475 Graphing the Standard Error of the Mean 475 Probabilities and Distributions 479 PROB 479 WEIBULL.DIST 479 Drawing Samples 480 Testing Independence: The True Use of CHISQ.TEST 481 Logarithmica Esoterica 484 What is a logarithm? 484 What is e? 486 LOGNORM.DIST 489 LOGNORM.INV 490 Array Function: LOGEST 491 Array Function: GROWTH 494 The logs of Gamma 497 Sorting Data 498 Part 6: Appendices 501 Appendix A: When Your Data Live Elsewhere 503 Appendix B: Tips for Teachers (and Learners) 507 Augmenting Analyses Is a Good Thing 507 Understanding ANOVA 508 Revisiting regression 510 Simulating Data Is Also a Good Thing 512 When All You Have Is a Graph 514 Appendix C: More on Excel Graphics 515 Tasting the Bubbly 515 Taking Stock 516 Scratching the Surface 518 On the Radar 519 Growing a Treemap and Bursting Some Sun 520 Building a Histogram 521 Ordering Columns: Pareto 522 Of Boxes and Whiskers 523 3D Maps 524 Filled Maps 527 Appendix D: The Analysis of Covariance 529 Covariance: A Closer Look 529 Why You Analyze Covariance 530 How You Analyze Covariance 531 ANCOVA in Excel 532 Method 1: ANOVA 533 Method 2: Regression 537 After the ANCOVA 540 And One More Thing 542 Index 545

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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 SynopsisThis new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.Trade Review“The versatility and efficiency of ggplot have led to the development of ggplot2 and this book which overviews the standard use and presentation secrets of functions developed in the last 5 years. … The book is written in an accessible manner and it is suitable for undergraduates, postgraduates and researchers with some R experience. All theoretical concepts are accompanied by code making it easy to learn by reproducing the examples.” (Irina Ioana Mohorianu, zbMATH 1397.62006, 2018)“The book is an excellent and very comprehensive manual of … one of the most popular R packages. It is currently the only book describing ggplot2 in such depth. The book contains many examples and is very nicely illustrated, demonstrating the strength of the package.” (Klaus Galensa, Computing Reviews, May, 2017)Table of ContentsIntroduction.- Getting Started with ggplot2.- Toolbox.- Mastering the Grammar.- Building a Plot Layer by Layer.- Scales, Axes and Legends.- Positioning.- Themes.- Data Analysis.- Data Transformation.- Modelling for Visualisation.- Programming with ggplot2.- Index.- R Code Index.

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Book SynopsisLearn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools

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Book SynopsisStraightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation. The book includes: · Original case studies and data sets · Practical exercises and lists of commands for each chapter · Downloadable Stata programmes created to work alongside chapters · A wide range of detailed applications using Stata · Step-by-step guidance on writing the relevant code. This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis. Trade ReviewNewly updated, now with more advanced content, this book remains a must have for those studying applied statistics. The book is practically orientated with intuitive theoretical explanations, a wide array "how-to-do-it" examples and an engaging narrative. You won’t’ be sorry! -- Franz BuschaThis is a most impressive teaching and learning resource. Mehmetoglu and Jakobsen expertly introduce introductory to advanced social science data analysis skills in a clear and engaging manner. This text teaches students how to do data analysis in a transparent and principled manner. -- Roxanne ConnellyMehmetoglu and Jakobsen′s book offers a concise, yet comprehensive, introduction to the statistical methods that are widely used in data analysis. In addition to presenting a thorough overview of the basics of conducting empirical research, the book also emphasizes how to use Stata to analyze data in practice. This book is an excellent starting point for those who are interested in empirical work. -- Hector H. SandovalTable of ContentsChapter 1. Research and Statistics Chapter 2. Introduction to Stata Chapter 3. Simple (Bivariate) Regression Chapter 4. Multiple Regression Chapter 5. Dummy-Variable Regression Chapter 6. Interaction/Moderation Effects Using Regression Chapter 7. Linear Regression Assumptions and Diagnostics Chapter 8. Logistic Regression Chapter 9. Survival Analysis Chapter 10. Multilevel Analysis Chapter 11. Panel Data Analysis Chapter 12. Time Series Analysis Chapter 13. Exploratory Factor Analysis Chapter 14. Structural Equation Modelling and Confirmatory Factor Analysis Chapter 15. Advanced Statistical Techniques Chapter 16. Programming and Dynamic Reporting Using Stata

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Book SynopsisThis accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI-including regression, neural networks, optimization, backpropagation, and Markov chains.

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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 SynopsisThis textbook presents an introduction to multiple linear regression, providing real-world data sets and practice problems. A practical working knowledge of applied statistical practice is developed through the use of these data sets and numerous case studies. The authors include a set of practice problems both at the end of each chapter and at the end of the book. Each example in the text is cross-referenced with the relevant data set, so that readers can load the data and follow the analysis in their own R sessions. The balance between theory and practice is evident in the list of problems, which vary in difficulty and purpose.This book is designed with teaching and learning in mind, featuring chapter introductions and summaries, exercises, short answers, and simple, clear examples. Focusing on the connections between generalized linear models (GLMs) and linear regression, the book also references advanced topics and tools that have not typically been included inTrade Review“This is a great book … . The book comprehensively covers almost everything you need to know or teach in this area. This book is an invaluable reference either as a classroom text or for the researcher’s bookshelf.” (Pablo Emilio Verde, ISCB News, iscb.info, Issue 69, July, 2020)“I congratulate the authors for making an important contribution in this field. … the book represents an excellent and very comprehensible introduction into the world of generalized linear models and is recommended for all readers who are looking for a practical introduction to this topic using R.” (Dominic Edelmann, Biometrical Journal, Vol. 62, 2020)“The book is targeted at students and notes it is appropriate for graduate students. It is also useful to the junior statistician needing to learn how to work a model they are unfamiliar with. The practicing and experienced statistician can use this as a quick reference for working a model they may have forgotten the specific of.” (James P. Howard II, zbMath 1416.62020, 2019)Table of ContentsStatistical models.- Linear regression models.- Linear regression models: diagnostics and model-building.- Beyond linear regression: the method of maximum likelihood.- Generalized linear models: structure.- Generalized linear models: estimation.- Generalized linear models: inference.- Generalized linear models: diagnostics.- Models for proportions: binomial GLMs.- Models for counts: Poisson and negative binomial GLMs.- Positive continuous data: gamma and inverse Gaussian GLMs.- Tweedie GLMs.- Extra problems.- Appendix A: Using R for data analysis.- Appendix B: The GLMsData package.- Index: Data sets.- Index: R commands.- Index: General Topics.

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Book SynopsisTrade Review"Data mining practitioners, here is your bible, the complete "driver's manual" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering, and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here. "Going beyond its responsibility as a reference book, the heavily-updated second edition also provides all-new, detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success. "What's more, this edition drills down on hot topics across seven new chapters, including deep learning and how to avert "b---s---" results. If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner." --Eric Siegel, Ph.D., founder of Predictive Analytics World and author of "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" "Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners." --Karl Rexer, PhD (President and Founder of Rexer Analytics, Boston, Massachusetts)Table of ContentsPart 1: History Of Phases Of Data Analysis, Basic Theory, And The Data Mining Process 1. The Background for Data Mining Practice 2. Theoretical Considerations for Data Mining 3. The Data Mining and Predictive Analytic Process 4. Data Understanding and Preparation 5. Feature Selection 6. Accessory Tools for Doing Data Mining Part 2: The Algorithms And Methods In Data Mining And Predictive Analytics And Some Domain Areas 7. Basic Algorithms for Data Mining: A Brief Overview 8. Advanced Algorithms for Data Mining 9. Classification 10. Numerical Prediction 11. Model Evaluation and Enhancement 12. Predictive Analytics for Population Health and Care 13. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors 14. Customer Response Modeling 15. Fraud Detection Part 3: Tutorials And Case Studies Tutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13 Tutorial B Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta) Tutorial C Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX) Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality Data Tutorial E Feature Selection in KNIME Tutorial F Medical/Business Tutorial Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F Tutorial H Data Prep 1-1: Merging Data Sources Tutorial I Data Prep 1–2: Data Description Tutorial J Data Prep 2-1: Data Cleaning and Recoding Tutorial K Data Prep 2-2: Dummy Coding Category Variables Tutorial L Data Prep 2-3: Outlier Handling Tutorial M Data Prep 3-1: Filling Missing Values With Constants Tutorial N Data Prep 3-2: Filling Missing Values With Formulas Tutorial O Data Prep 3-3: Filling Missing Values With a Model Tutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner Tutorial Q Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10 Tutorial R Example With C&RT to Predict and Display Possible Structural Relationships Tutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client Part 4: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and the Future, and Advanced Processes 16. The Apparent Paradox of Complexity in Ensemble Modeling 17. The "Right Model" for the "Right Purpose": When Less Is Good Enough 18. A Data Preparation Cookbook 19. Deep Learning 20. Significance versus Luck in the Age of Mining: The Issues of P-Value "Significance" and "Ways to Test Significance of Our Predictive Analytic Models" 21. Ethics and Data Analytics 22. IBM Watson

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Book SynopsisTable of Contents1. Getting Started 2. Numbers, Expressions and Functions 3. Calculus 4. Introduction to Lists and Tables 5. Nested Lists: Matrices and Vectors 6. Applications Related to Ordinary and Partial Differential Equations

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Book SynopsisComputational complexity is one of the most beautiful fields of modern mathematics, and it is increasingly relevant to other sciences ranging from physics to biology. But this beauty is often buried underneath layers of unnecessary formalism, and exciting recent results like interactive proofs, phase transitions, and quantum computing are usually considered too advanced for the typical student. This book bridges these gaps by explaining the deep ideas of theoretical computer science in a clear and enjoyable fashion, making them accessible to non-computer scientists and to computer scientists who finally want to appreciate their field from a new point of view. The authors start with a lucid and playful explanation of the P vs. NP problem, explaining why it is so fundamental, and so hard to resolve. They then lead the reader through the complexity of mazes and games; optimization in theory and practice; randomized algorithms, interactive proofs, and pseudorandomness; Markov chains and phase transitions; and the outer reaches of quantum computing. At every turn, they use a minimum of formalism, providing explanations that are both deep and accessible. The book is intended for graduate and undergraduate students, scientists from other areas who have long wanted to understand this subject, and experts who want to fall in love with this field all over again.Trade ReviewA creative, insightful, and accessible introduction to the theory of computing, written with a keen eye toward the frontiers of the field and a vivid enthusiasm for the subject matter. * Jon Kleinberg, Cornell University *To put it bluntly: this book rocks! It's 900+ pages of awesome. It somehow manages to combine the fun of a popular book with the intellectual heft of a textbook, so much so that I don't know what to call it (but whatever the genre is, there needs to be more of it!). * Scott Aaronson, Massachusetts Institute of Technology *Moore and Mertens guide the reader through the interesting field of computational complexity in a clear, broadly accessible and informal manner, while systematically explaining the main concepts and approaches in this area and the existing links to other disciplines. The book is comprehensive and can be easily used as a textbook, at both advanced undergraduate and postgraduate levels, but is equally useful for researchers in neighbouring disciplines, such as statistical physics [...]. Some of the material covered, such as approximability issues and Probabilistically Checkable Proofs is typically not presented in books of this type, and the authors do an excellent job in presenting them very clearly and convincingly. * David Saad, Aston University, Birmingham *A treasure trove of ideas, concepts and information on algorithms and complexity theory. Serious material presented in the most delightful manner! * Vijay Vazirani, Georgia Instituute of Technology *In a class by itself - in The Nature of Computation, Cristopher Moore and Stephan Mertens have produced one of the most successful attempts to capture the broad scope and intellectual depth of theoretical computer science as it is practiced today. The Nature of Computation is one of those books you can open to a random page and find something amazing, surprising and, often, very funny. * American Scientist *a comprehensive, accessible, and highly enjoyable book that conveys the key intellectual contributions of the theory of computing ... a valuable resource for any educator * Haris Aziz, SIGACT *The book is highly recommended for all interested readers: in or out of courses, students undergraduate or graduate, researchers in other fields eager to learn the subject, or scholars already in the field who wish to enrich their current understanding. It makes for a great textbook in a conventional theory of computing course, as I can testify from recent personal experience (I used it once; Ill use it again!). With its broad and deep wealth of information, it would be a top contender for one of my desert island books.TNoC speaks directly, clearly, convincingly, and entetainingly, but also goes much further: it inspires. * Frederic Green, SIGACT *Table of Contents1. Prologue ; 2. The Basics ; 3. Insights and Algorithms ; 4. Needles in a Haystack: The class NP ; 5. Who is the Hardest One of All: NP-Completeness ; 6. The Deep Question: P vs. NP ; 7. Memory, Paths and games ; 8. Grand Unified Theory of Computation ; 9. Simply the Best: Optimization ; 10. The Power of Randomness ; 11. Random Walks and Rapid Mixing ; 12. Counting, Sampling, and Statistical Physics ; 13. When Formulas Freeze: Phase Transitions in Computation ; 14. Quantum Computing ; 15. Epilogue ; 16. Appendix: Mathematical Tools

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Book SynopsisAn introduction to a popular programming language for neuroscience research, taking the reader from beginning to intermediate and advanced levels of MATLAB programming.MATLAB is one of the most popular programming languages for neuroscience and psychology research. Its balance of usability, visualization, and widespread use makes it one of the most powerful tools in a scientist's toolbox. In this book, Mike Cohen teaches brain scientists how to program in MATLAB, with a focus on applications most commonly used in neuroscience and psychology. Although most MATLAB tutorials will abandon users at the beginner's level, leaving them to sink or swim, MATLAB for Brain and Cognitive Scientists takes readers from beginning to intermediate and advanced levels of MATLAB programming, helping them gain real expertise in applications that they will use in their work.The book offers a mix of instructive text and rigorous explanations of MATLAB code along with programming tips

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Book SynopsisA self-contained introduction to abstract interpretation-based static analysis, an essential resource for students, developers, and users.Static program analysis, or static analysis, aims to discover semantic properties of programs without running them. It plays an important role in all phases of development, including verification of specifications and programs, the synthesis of optimized code, and the refactoring and maintenance of software applications. This book offers a self-contained introduction to static analysis, covering the basics of both theoretical foundations and practical considerations in the use of static analysis tools. By offering a quick and comprehensive introduction for nonspecialists, the book fills a notable gap in the literature, which until now has consisted largely of scientific articles on advanced topics.The text covers the mathematical foundations of static analysis, including semantics, semantic abstraction, and computation of program inv

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Book SynopsisDiscover everything you want to know about Microsoft's newest version of Windows in this easy-to-use guide; from the most essential tasks that you'll want to perform, to solving the most common problems you'll encounter.Table of ContentsTop 10 Windows 8 Tips for the Over 50s 1. Shut Down Windows 2. Turn Live Tiles Off or On 3. Move Among Apps Quickly 4. Make icons easier to see in File Explorer 5. Back up data quickly and easily 6. Connect to a Free Wireless Hot Spot 7. Pin a Website to the Start Screen 8. Email a Photo 9. Install a digital camera, web cam, or smart phone 10. Install Anti-Virus Software 1 Learn Windows 8 Basics Know What Kind of Device you have Set up Windows 8 Consider a Microsoft Account Log In to Windows 8 Explore the Start Screen Open and Close an App Access Charms Understand Charms Access the Traditional Desktop Explore File Explorer Switch to a Microsoft Account Shut Down Windows 2 Make Windows 8 Easier to Use, See, and Navigate Change the Volume Change the Screen Resolution Personalize the Color of the Start Screen Background Turn Live Tiles Off or On Make App Tiles Larger or Smaller Reposition Apps on the Start Screen Add a Tile to the Start Screen Remove a Tile from the Start Screen Log In with Fewer Keystrokes Create Shortcuts on the Desktop Pin Items to the Taskbar Explore Accessibility Options Explore Touch Techniques 3 Use Apps to Be More Efficient Check Your Local Weather Throw Away your Physical Maps Travel without Leaving your Home Get the Latest Sports News and Follow a Team Switch to a Digital Personal Calendar Create a New Event in Calendar Explore your Piece of the Cloud Upload a File to Sky Drive Access Your Files on Sky Drive from Anywhere Shop the Windows Store Use your Free App Move among Open Apps Quickly 4 Use desktop Applications Find the desktop Applications Write a letter with Notepad Save a letter with Notepad Print a letter with Notepad Use the calculator Take a picture of what’s on the screen Share a screen shot Record and save a sound clip Play a sound clip Explore other desktop apps 5 Locate and Manage the Data you keep and Acquire Explore your Libraries Save data to a library Create a Folder or Subfolder Copy or Move a File or Folder Delete a File or Folder Explore your personal folders Search for a File Browse for a file from a desktop app Change the Size of an Open Window Use Snap, Peek, and Shake Make icons easier to see Move data to Public Folders Back up data quickl

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Book SynopsisTable of Contents1. Statistical analysis approach for the quality assessment of open-source software Yoshinobu Tamura and Shigeru Yamada 2. Analytical modeling and performance evaluation of SIP signaling protocol: Analytical modeling of SIP Nikesh Choudhary, Vandana Khaitan (nee Gupta), and Vaneeta Goel 3. An empirical validation for predicting bugs and the release time of open source software using entropy measures—Software reliability growth models Anjali Munde 4. Risk assessment of starting air system of marine diesel engine using fuzzy failure mode and effects analysis Rajesh S. Prabhu Gaonkar and Sunay P. Pai 5. Test scenario generator learning for model-based testing of mobile robots Gert Kanter and Marti Ingmar Liibert 6. Testing effort-dependent software reliability growth model using time lag functions under distributed environment Sudeept Singh Yadav, Avneesh Kumar, Prashant Johri, and J.N. Singh 7. Design and performance analysis of MIMO PID controllers for a paper machine subsystem Niharika Varshney, Parvesh Saini, and Ashutosh Dixit 8. Network and security leveraging IoT and image processing: A quantum leap forward Ajay Sudhir Bale, S. Saravana Kumar, S. Varun Yogi, Swetha Vura, R. Baby Chithra, N. Vinay, and P. Pravesh 9. Modeling software patching process inculcating the impact of vulnerabilities discovered and disclosed Deepti Aggrawal, Jasmine Kaur, and Adarsh Anand 10. Extension of software reliability growth models by several testing-time functions Yuka Minamino, Shinji Inoue, and Shigeru Yamada 11. A semi-Markov model of a system working under uncertainty R.K. Bhardwaj, Purnima Sonker, and Ravinder Singh 12. Design and evaluation of parallel-series IRM system Sridhar Akiri, P. Sasikala, Pavan Kumar Subbara, and VSS Yadavalli 13. Modeling and availability assessment of smart building automation systems with multigoal maintenance Yuriy Ponochovniy, Vyacheslav Kharchenko, and Olga Morozova 14. A study of bitcoin and Ethereum blockchains in the context of client types, transactions, and underlying network architecture Rohaila Naaz and Ashendra Kumar Saxena 15. High assurance software architecture and design Muhammad Ehsan Rana and Omar S. Saleh 16. Online condition monitoring and maintenance of photovoltaic system Neeraj Khera 17. Fault diagnosis and fault tolerance Afaq Ahmad and Sayyid Samir Al Busaidi 18. True power loss diminution by Improved Grasshopper Optimization Algorithm Lenin Kanagasabai 19. Security analytics Vani Rajasekar, J Premalatha, and Rajesh Kumar Dhanaraj 20. Stochastic modeling of the mean time between software failures: A review Gabriel Pena, Veronica Moreno, and Nestor Barraza 21. Inliers prone distributions: Perspectives and future scopes K. Muralidharan and Pratima Bavagosai 22. Integration of TPM, RCM, and CBM: A practical approach applied in Shipbuilding industry Rupesh Kumtekar, Swapnil Kamble, and Suraj Rane 23. Revolutionizing the internet of things with swarm intelligence Abhishek Kumar, Jyotir Moy Chatterjee, Manju Payal, and Pramod Singh Rathore 24. Security and challenges in IoT-enabled systems S. Kala and S. Nalesh 25. Provably correct aspect-oriented modeling with UPPAAL timed automata Juri Vain, Leonidas Tsiopoulos, and Gert Kanter 26. Relevance of data mining techniques in real life Palwinder Kaur Mangat and Kamaljit Singh Saini 27. D-PPSOK clustering algorithm with data sampling for clustering big data analysis C. Suresh Gnana Dhas, N. Yuvaraj, N.V. Kousik, and Tadele Degefa Geleto 28. A review on optimal placement of phasor measurement unit (PMU) Ashutosh Dixit, Arindam Chowdhury, and Parvesh Saini 29. Effective motivational factors and comprehensive study of information security and policy challenges M. Arvindhan 30. Integration of wireless communication technologies in internet of vehicles for handover decision and network selection Shaik Mazhar Hussain, Kamaludin Mohamad Yusof, Afaq Ahmad, and Shaik Ashfaq Hussain 31. Modeling HIV-TB coinfection with illegal immigrants and its stability analysis Rajinder Sharma

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Book SynopsisThis book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines.Key Features: A general framework for learning sparse gTable of Contents1. Introduction to Sparse Graphical Models 2. Gaussian Graphical Models 3. Gaussian Graphical Modeling with Missing Data 4. Gaussian Graphical Modeling for Heterogeneous Data 5. Poisson Graphical Models 6. Mixed Graphical Models 7. Joint Estimation of Multiple Graphical Models 8. Nonlinear and Non-Gaussian Graphical Models 9. High-Dimensional Inference with the Aid of Sparse Graphical Modeling 10. Appendix

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Book SynopsisFrom a review of the first edition: Modern Data Science with R is rich with examples and is guided by a strong narrative voice. What's more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician).Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to undTrade Review"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." (Hunter Glanz Cal Poly San Luis Obispo)"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." (Lynn Collen, St. Cloud Stat Univ.)"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." (Matthew Beckman, Penn State University)"[...] To answer a wide range of modern research questions, this book by Baumer, Kaplan, and Horton features an excellent introduction to data wrangling, visualization, statistical modeling, machine learning, and other advanced statistical applications through the RStudio environment following the tidyverse syntax. [...] Overall, Modern Data Science with R, 2nd edition serves as an excellent introductory resource to help develop techniques to extract, transform, visualize, and learn from datasets through the R environment. It focuses on implementing those techniques in R and does not provide a theoretical background for the discussed methods. The book will be a perfect reference for a broad audience ranging from undergraduates in data science courses to advanced graduate students and professionals from a variety of research fields."-Kohma Arai and Vyacheslav Lyubchich, in Technometrics, July 2022"Overall, I enjoyed reading this book. The authors were very good at creating a complete tool for studying data science. Therefore, I recommend this book, for its content, writing, and organization, to graduate students in data science and statistics. I also recommend the book to professionals who should prepare themselves for the challenges they are going to face in the future with the voluminous and heterogenous amount of data that should be timely analyzed to extract meaningful information to guide action."-Georgios Nikolopoulos, in ISCB News, June 2022"The authors have successfully completed the job of choosing the content with relevant topics and, deciding the extent of knowledge to be delivered, and finally, putting them in an understandable sequence. This is a well-written book and does not cover much theory. .. The book’s second edition contents are updated, expanded, revised, split, rewritten and rearranged compared to the first edition. The key changes are the use of recently developed R packages, .... (and) updated exercises in the chapters ..."-Shalabh,in Journal of the Royal Statistical Society Series A, August 2021"[This book] provides an excellent basis for statisticians who want to dig deeper into, for example, data handling, for computer scientists who aim to strengthen their knowledge of statistical methods as well as for all other researchers who are interested in data science in general. ... Each section is structured as an interplay between R-code and explanatory text for understanding. The division into several stand-alone segments is an advantage, because the reader may easily choose the section she or he is interested in without missing relevant information. A key feature of the book is its focus on different example data sets that are available via R-packages or from URLs that are embedded in the text. These data sets are used to illustrate the methodology presented using R-code. Their availability allows the reader to reproduce the code while working with the book. ... It can be warmly recommended to practical researchers who seek a comprehensive overview of different topics in data science with focus on implementations in R."-Annika Hoyer, in Biometrical Journal, August 2021"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." -Hunter Glanz, Cal Poly San Luis Obispo"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." -Lynn Collen, St. Cloud Stat University"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." -Matthew Beckman, Penn State University"The authors have covered almost all aspects of data science, a revolutionary field that marries elements of computational thinking and traditional statistical theory. The book can thus equip the readers with the necessary knowledge and skills to extract data from a variety of sources, restructure observations in a form that allows analysis, store data in efficient databases, and work effectively on massive and complex data sets in order to produce actionable information."- Georgios Nikolopoulos, University of Cyprus, ISCB Book Reviews, June 2022.Table of ContentsI Part I: Introduction to Data Science. 1. Prologue: Why data science? 2. Data visualization. 3. A grammar for graphics. 4. Data wrangling on one table. 5. Data wrangling on multiple tables. 6. Tidy data. 7. Iteration. 8. Data science ethics. II. Part II: Statistics and Modeling. 9. Statistical foundations. 10. Predictive modeling. 11. Supervised learning. 12. Unsupervised learning. 13. Simulation. III Part III: Topics in Data Science. 14. Dynamic and customized data graphics. 15. Database querying using SQL. 16. Database administration. 17. Working with spatial data. 18.Geospatial computations. 19. Text as data. 20. Network science. IV Part IV: Appendices.

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Book SynopsisThe goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. A useful feature of the presentation is the inclusion of nontrivial data sets illustrating the richness of potential applications to problems in the biological, physical, and social sciences as well as medicine. The text presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis.Numerous examples using data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems. The text can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability skills, and math skills at the Trade Review"The intended audience of the book are mathematics undergraduates taking a one semester course on time series. . . The authors frame learning time series primarily by extending concepts from linear models. Personally, I favour this approach, since it allows the book to clearly signpost similarities and differences between concepts in both topics and provides a natural learning progression from what most undergraduate students will already be familiar with . . .This book successfully delivers a practical tool-based approach to time series analysis at an introductory level, complementing the existing texts from the authors, which are aimed at a more advanced audience."~Matthew Nunes, Journal Times Series AnalysisTable of Contents1. Time Series Characteristics. 2. Time Series Regression and EDA. 3. ARIMA Models. 4. Spectral Analysis and Filtering. 5. Some Additional Topics.

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Book SynopsisBayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest.The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Table of ContentsPart I. Overview of Bayesian inference in causal inference and missing data and identifiability. 1. Overview of causal inference. 2. Overview of missing data. 3. Overview of Bayesian Inference for Missing Data and Causal Inference. Part II. Bayesian nonparametrics for causal inference and missing data. 4. Identifiability and Sensitivity Analysis. 5. Bayesian Decision Trees and their Ensembles. Part III. Identification and sensitivity analysis. 6. Dirichlet Process Mixtures and extensions. 7. Gaussian process prior and Dependent Dirichlet processes. 8. Causal Inference on Quantiles using Propensity scores. 9. Causal Inference with a point treatment using an EDPM model. 10. DDP+GP for causal inference using marginal structural models. 11. DPMs for Dropout in Longitudinal Studies. 12. DPMs for Non-Monotone Missingness.

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Book SynopsisStatistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming.The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining dataTrade Review"This book is useful for people who want to learn SAS programing, and assumes the students have knowledge of multiple linear regression and one-way ANOVA models.…The second edition has added a chapter on text processing, and reorganized the chapter order…Some topics that are relevant for the SAS Base and Certifications exams are covered, and a nice feature is the highlighting of programing tips in gray." ~Technometrics"This is a very complete book for programming SAS in statistical analyses. This second edition offers the possibility to debug some programs and provides new examples and applications, which are very useful. This book is a very useful companion tool for students or beginners in SAS, or for more experienced statisticians who already use SAS for statistical analyses."~ISCB NewsTable of ContentsContentsPreface ..............................................................................................................................................ixAcknowledgments ...................................................................................................................... xiiiAuthor .............................................................................................................................................xv1. Structuring, Implementing, and Debugging Programs to Learn about Data ...........11.1 Statistical Programming ................................................................................................11.2 Learning from Constructed, Artificial Data ...............................................................2Processing a Particular Data Set—Extracting Variable Names from aColumn of an Input Data Set.........................................................................................2Learning More about Unfamiliar Statistical Methods—Linear MixedEffects Models .................................................................................................................5Improving Your Intuition about Statistical Theory— Sampling Distributionof Means ...........................................................................................................................81.3 Good Programming Practice ...................................................................................... 11Document Your Programs! .......................................................................................... 11Use Meaningful Variable Names ................................................................................ 13Use a Variety of CaSeS in Program Statements ........................................................ 14Indent Program Statements That Naturally Go Together ....................................... 141.4 SAS Program Structure ................................................................................................ 151.5 What Is a SAS Data Set? ............................................................................................... 211.6 Internally Documenting SAS Programs ....................................................................221.7 Basic Debugging ...........................................................................................................231.8 Getting Help ..................................................................................................................27Using Help in SAS ........................................................................................................27Getting Help from a Web Browser Search .................................................................291.9 Exercises .........................................................................................................................292. Reading, Creating, and Formatting Data Sets ................................................................ 312.1 What Does a SAS DATA Step Do? .............................................................................. 312.2 Reading Data from External Files ..............................................................................33Reading Data Directly as Part of a Program—Anyone for Datalines? .................34Reading Data Sets Saved as Text—INFILE Can Be Your Friend (PROCIMPORT Too!) ................................................................................................................38Sometimes, Variables Are in Particular Columns or in Particular Formats .........402.3 Reading CSV, Excel, and TEXT Files .......................................................................... 412.4 Temporary versus Permanent Status of Data Sets ...................................................432.5 Formatting and Labeling Variables ............................................................................46Using Formats to Read and Display Variable Values ..............................................46Internal Representations and Output Displays ........................................................49Character, Numeric, Time, and Date Formats ..........................................................532.6 User-Defined Formatting .............................................................................................58Saving Formats for Later Use ......................................................................................632.7 Recoding and Transforming Variables in a DATA Step ........................................66Indicator Variables ......................................................................................................682.8 Writing Out a File or Making a Simple Report ......................................................73Simple Report Generation .........................................................................................73Exporting a File ...........................................................................................................772.9 Exercises .......................................................................................................................803. Programming a DATA Step ................................................................................................833.1 Writing Programs by Subdividing Tasks ................................................................83Estimate the Probability That a Randomly Selected 30- to 39-Year-OldMale Is Taller than a Randomly Selected Female of the Same Age .....................83Conditional Execution ...........................................................................................84Looping to Repeat a Task ......................................................................................86Returning to the Height Probability Simulation ............................................... 873.2 Ordering How Tasks Are Done ................................................................................90Missing Data in Functions .........................................................................................923.3 Indexable Lists of Variables (Also Known as Arrays) ...........................................93Defining Values in the Variable List .........................................................................93Inputting Values in the Variable List ........................................................................94Reassign Missing Value Codes for Numeric Variables “.” ...................................95Recoding Missing Values for All Numeric and Character Variables ..................953.4 Functions Associated with Statistical Distributions .............................................963.5 Generating Variables Using Random Number Generators ................................ 1023.6 Remembering Variable Values across Observations ........................................... 105Processing Multiple Observations for a Single Observation .............................. 1063.7 Case Study 1: Is the Two-Sample t-Test Robust to Violations of theHeterogeneous Variance Assumption? ................................................................. 109Case Study 1 (Revisited with DATA Step Programming) .................................. 1183.8 Efficiency Considerations—How Long Does It Take? .........................................1223.9 Case Study 2: Monte Carlo Integration to Estimate an Integral ........................ 1233.10 Case Study 3: Simple Percentile-Based Bootstrap ................................................ 1283.11 Case Study 4: Randomization Test for the Equality of Two Populations ......... 1303.12 Exercises ..................................................................................................................... 1344. Combining, Extracting, and Reshaping Data ............................................................... 1374.1 Adding Observations by SET-ing Data Sets.......................................................... 1374.2 Adding Variables by MERGE-ing Data Sets ......................................................... 1404.3 Working with Tables in PROC SQL ....................................................................... 1484.4 Converting Wide to Long Formats ......................................................................... 1614.5 Converting Long to Wide Formats ......................................................................... 1644.6 Case Study: Reshaping a World Bank Data Set .................................................... 1664.7 Building Training and Validation Data Sets ......................................................... 1754.8 Exercises ..................................................................................................................... 1794.9 Self-study Lab ............................................................................................................ 1805. Macro Programming .......................................................................................................... 1915.1 What Is a Macro and Why Would You Use It? ..................................................... 1915.2 Motivation for Macros: Numerical Integration to DetermineP(0 < Z < 1.645) ......................................................................................................... 1915.3 Processing Macros .................................................................................................... 1955.4 Macro Variables, Parameters, and Functions........................................................ 1955.5 Conditional Execution, Looping, and Macros ...................................................... 198More Complicated Macro Variable Construction ................................................203Changing Locations in a Macro during Execution ..............................................2045.6 Debugging Macro Code and Programs.................................................................206Write Out Values of Macro Variables .....................................................................206Useful SAS Options for Debugging Macros ......................................................... 2075.7 Saving Macros ........................................................................................................... 2115.8 Functions and Routines for Macros ....................................................................... 2115.9 Case Study: Macro for Constructing Training and Test Data Set for ModelComparison ............................................................................................................... 2165.10 Case Study: Processing Multiple Data Sets ...........................................................2235.11 Exercises .....................................................................................................................2276. Customizing Output and Generating Data Visualizations .......................................2296.1 Using the Output Delivery System ........................................................................229Basic Ideas ..................................................................................................................229Destinations—RTF, HTML, PDF, and More! .........................................................230What’s Produced and How to Select It ..................................................................235Another Destination That Stat Programmers Should Visit—OUTPUT ............ 2436.2 Graphics in SAS ......................................................................................................... 2496.3 ODS Statistical Graphics ..........................................................................................2506.4 Modifying Graphics Using the ODS Graphics Editor ......................................... 2576.5 Graphing with Styles and Templates .....................................................................2606.6 Statistical Graphics—Entering the Land of SG Procedures ............................... 266SGPLOT ...................................................................................................................... 266SGPANEL ................................................................................................................... 269SGSCATTER .............................................................................................................. 2716.7 Case Study: Using the SG Procedures ................................................................... 2736.8 Enhancing SG Displays—Options with SG Procedure Statements .................. 2796.9 Using Annotate Data Sets to Enhance SG Displays ............................................2846.10 Using Attribute Maps to Enhance SG Displays ................................................... 2876.11 Exercises .....................................................................................................................2907. Processing Text .................................................................................................................... 2937.1 Cleaning and Processing Text Data ....................................................................... 2937.2 Starting with Character Functions ......................................................................... 2937.3 Processing Text .......................................................................................................... 2987.4 Case Study: Sentiment in State of the Union Addresses .....................................3027.5 Case Study: Reading Text from a Web Page .........................................................3097.6 Regular Expressions ................................................................................................. 3157.7 Case Study (Revisited)—Applying Regular Expressions ................................... 3197.8 Exercises ..................................................................................................................... 3218. Programming with Matrices and Vectors ..................................................................... 3238.1 Defining a Matrix and Subscripting ...................................................................... 3238.2 Using Diagonal Matrices and Stacking Matrices ................................................. 3298.3 Using Elementwise Operations, Repeating, and Multiplying Matrices ........... 3328.4 Importing a Data Set into SAS/IML and Exporting Matrices fromSAS/IML to a Data Set .............................................................................................333Creating Matrices from SAS Data Sets and Vice Versa ........................................3338.5 Case Study 1: Monte Carlo Integration to Estimate π ..........................................3368.6 Case Study 2: Bisection Root Finder ...................................................................... 3378.7 Case Study 3: Randomization Test Using Matrices Imported from PROCPLAN ..........................................................................................................................3408.8 Case Study 4: SAS/IML Module to Implement Monte Carlo Integrationto Estimate π ..............................................................................................................3428.9 Storing and Loading SAS/IML Modules ..............................................................3448.10 SAS/IML and R .........................................................................................................3458.11 Exercises .....................................................................................................................350References ...................................................................................................................................355Index ............................................................................................................................................. 357

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Book SynopsisUnderstanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways.Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed just-in-time within chapters <Trade Review"...The authors suggest their book is suitable for those who are “research-oriented”, regardless of any prior advanced training in statistics...I particularly like the emphasis on assumptions. Rather than discuss regression in idealized terms, Westfall and Arias are upfront about why assumptions are often wrong in practice, and what an analyst can do about violations. These discussions are woven into many of the chapters, and in some cases, they are featured in stand-alone chapters...I am a fan of learning statistics by doing, so the large amount of R code woven into the book’s chapters and the hands-on exercises at the end of each chapter are valuable and a welcomed feature of the book...To me, this textbook would be most suitable for a one-semester survey course in statistical methods for students outside of biostatistics or statistics. A motivated student could even use this book for self-study...Overall, I believe this is a worthwhile addition to the literature."- Ryan Andrews, ISCB News, June 2021 Table of Contents1. Introduction to Regression Models 2. Estimating Regression Model Parameters3. The Classical Model and Its Consequences4. Evaluating Assumptions5. Transformations6. The Multiple Regression Model7. Multiple Regression from the Matrix Point of View8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity9. Polynomial Models and Interaction (Moderator) Analysis10. ANOVA, ANCOVA, and Other Applications of Indicator Variables11. Variable Selection12. Heteroscedasticity and Non-independence13. Models for Binary, Nominal, and Ordinal Response Variables14. Models for Poisson and Negative Binomial Response15. Censored Data Models16. Outliers, Identification, Problems, and Remedies (Good and Bad)17. Neural Network Regression 18. Regression Trees19. Bookend

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Book SynopsisFrom the Reviews[This book] contains an excellent blend of both Shiny-specific topics and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters.Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)[This] book is a gradual and pleasant invitation to the production-ready shiny apps world. It exposes a comprehensive and robust workflow powered by the {golem} package. [It] fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one sitting. In the industry world, where processes robustness is a key toward productivity, this book will indubitably have a tremendous impact.David Granjon, Sr. Expert Data Science, NovartisPresented in full color, Engineering Production-Grade ShinTrade Review"ThinkR’s book is a gradual and pleasant invitation to the production-ready shiny apps world. It focuses on the unfortunately too often forgotten general principles necessary to be successful in this quest, before exposing a comprehensive and robust workflow powered by the {golem} package. This books fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one sitting. Readers will appreciate the number of exclusive references like {shinypsum}, {gargoyle}, {crrry} and {dockerfiler} that will definitely help them to reach the production-ready graal. In the industry world, where processes robustness is a key toward productivity, this book will indubitably have a tremendous impact." – David Granjon, Sr. Expert Data Science, Novartis"[This book] contains an excellent blend of both Shiny-specific topics … and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters…."– Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)Table of Contents1. About Successful Shiny Apps. 2. Planning Ahead. 3. Structuring your Project. 4. Introduction to {golem}. 5. The workflow. 6. UX Matters. 7. Don’t rush into coding. 8. Setting up for success with {golem} (#settingupsuccess). 9. Building an “ipsum-app”. 10. Building the app with {golem} 11. Build yourself a safety net. 12. Version Control. 13. Deploy your application. 14. The Need for Optimization. 15. Common Application Caveats. 16. Optimizing {shiny} Code. 17. Using JavaScript. 18. A Gentle Introduction to CSS. Appendix.

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Book SynopsisThis book is designed to provide a range of examples of how to extend the functionality of your R Markdown documents. As a cookbook, this guide is recommended to new or intermediate R Markdown users.Trade Review"This book will be a popular addition on the desk of many professionals who regularly produce technical documents in the R language."~Carl Boettiger, University of California, Berkeley"Novices will benefit from reading this book because they will quickly find high quality solutions to the most common issues that users encounter. Experts will benefit because it will save them from constantly trying to rediscover ‘that thread that explains how to do that one thing.’"~ Dr. John Blischak, University of Chicago"Though I am a frequent and longtime user of RMarkdown, there were multiple points where I learned new things and capabilities that I’d never seen. Sometimes incredible features are ‘hidden"; this book eposes features and tricks that this seasoned RMarkdown user was surprised and excited by."~ Sharla Gelfand, R and Shiny Developer"This book is useful for R stats users who, while using RMarkdown, come across certain problems that can be solved. It also provides advanced users with additional options to further customize RMarkdown to their personal needs. The many subchapters address a number of issues that a large majority of users can use as a reference book for their needs. There are examples for every user, no matter what previous knowledge is available and therefore this book is useful for any user group of markdown."~Dr. Johannes Friedrich"This book is interesting for a wide level of RMarkdown users."~MSc. Yasumoto, Atsushi, HACARUS Inc.'This book is an excellent resource for promoting the applications of R software. This book can be used more as a reference book for R Markdown and should find a place on the library shelves.'- Shalabh, Journal of the Royal Statistical Society, August 2021 https://doi.org/10.1111/rssa.12743'The book covers an enormous terrain: not only straightforward use with R, but also with other languages and system like Python, SAS and Stata. Specialized output to LaTeX and custom CSS-styled HTML is also covered. Typesetting beautiful tables is described extensively. And if you want to send emails from R, you will find the instructions here. This is a good cookbook, with many delicious recipes, from simple snacks to gourmet style dishes.'- Paul Eilers, International Society for Clinical Biostatistics, 72, 2021Table of Contents Installation 2. Conceptual Overview. 3. Basics. 4. Document Elements. 5. Formatting. 6. LaTeX Output. 7. HTML Output. 8. Word. 9. Multiple Output Formats. 10. Tables. 11. Chunk Options. 12. Output Hooks (*). 13. Chunk Hooks (*). 14. Miscellaneous knitr Tricks. 15. Other Languages. 16. Managing Projects. 17. Workflow.

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Book SynopsisPractical introduction to crime mapping and spatial data analysis using R and R Studio. Crime mapping and analysis of crime problems using spatially explicit data has become a central feature of law enforcement agencies across the world. Criminology degrees have begun to adapt their curriculums to foster the skills required for these jobs.Trade Review"I think overall the book is pitched perfectly and the step by step approach with code will act as an excellent training resources as well as reference guide.”-Ruth Weir, City, University of London"Overall, this is a great book! It is written in an accessible style, is up to date and covers the foundational material one would want a student to understand. As an experienced R user, I was delighted to learn something. Staying abreast of the fast-developing packages is nearly a full-time job, so I see this book as highly useful to many readers. The authors do a great job illustrating the main concepts of import but also pointing readers to places to follow up for more detailed treatments.”-Michael Townsley, Professor of Criminology and Criminal Justice, Griffith UniversityTable of Contents1. Producing your First Crime Map 2. Basic Geospatial Operations in R 3. Mapping Rates and Counts 4. Variations of Thematic Mapping 5. Basics of Cartographic Design: Elements of a Map 6. Time Matters 7. Spatial Point Patterns of Crime Events 8. Crime Along Spatial Networks 9. Spatial Dependence and Autocorrelation 10. Detecting Hot Spots and Repeats 11. Spatial Regression Models 12. Spatial Heterogeneity and Regression 13. Appendix: A Quick Intro to R and RStudio 14. Appendix B: Regression Analysis (A Refresher) 15. Appendix C: Sourcing Geographical Data for Crime Analysis

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Book SynopsisComputer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-Dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-Dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.Trade ReviewFrom the reviews:"The book is composed of 22 chapters. … This is a very readable book. … Kay’s book undoubtedly will see its greatest use in engineering schools, but I think it would work nicely in other settings as well. … It is written in a clear and informal style that students will appreciate, its coverage is excellent, and the author’s stated objective (to lessen the difficulty that students usually experience assimilating and applying probability and random processes) will, I predict, be met." (Ralph P. Russo, The American Statistician, Vol. 62 (2), May, 2008)“Kay’s book occupies a unique place in the overcrowded market of textbooks on probability and random processes. … This new textbook is a breath of fresh air in the market of books devoted to probability and random processes. The book lives up to its ambition of setting a new standard for a modern, computer-based treatment of the subject. … I fully recommend its use in undergraduate and first-year graduate courses.” (Osvaldo Simeone, IEEE Control Systems Magazine, Vol. 27, June, 2007)Table of ContentsComputer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-Dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-Dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.

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Book SynopsisThe fun and easy way to learn to use this leading business intelligence tool Written by an author team who is directly involved with SAS, this easy-to-follow guide is fully updated for the latest release of SAS and covers just what you need to put this popular software to work in your business. SAS allows any business or enterprise to improve data delivery, analysis, reporting, movement across a company, data mining, forecasting, statistical analysis, and more. SAS For Dummies, 2nd Edition gives you the necessary background on what SAS can do for you and explains how to use the Enterprise Guide. SAS provides statistical and data analysis tools to help you deal with all kinds of data: operational, financial, performance, and more Places special emphasis on Enterprise Guide and other analytical tools, covering all commonly used features Covers all commonly used features and shows you the practical applications you can put to worTable of ContentsIntroduction. Part I: Welcome to SAS! Chapter 1:Touring the Wonderful World of SAS. Chapter 2: Your Connection to SAS: Using SAS Enterprise Guide. Chapter 3: Six-Minute Abs: Getting Miraculous Results with SAS. Part II: Gathering Data and Presenting Information. Chapter 4: Accessing Data: Oh, the Choices! Chapter 5: Managing Data: I Can Do That? Chapter 6: Show Me a Report in Less Than a Minute. Chapter 7: Graphs: More Value with SAS. Part III: Impressing Your Boss with Your SAS Business Intelligence. Chapter 8: A Painless Introduction to Analytics. Chapter 9: More Analytics to Enlighten and Entertain. Chapter 10: Data Mining: Making the Leap from Guesses to Smart Choices. Part IV: Enhancing and Sharing Your SAS Masterpieces. Chapter 11: Leveraging Work from SAS to Those Less Fortunate. Chapter 12: Use OLAP and Impress Your Coworkers. Chapter 13: Supercharge Microsoft Offi ce with SAS. Chapter 14: Web Reporting Fever: SAS Has That Covered. Part V: Getting SAS Ready to Rock and Roll. Chapter 15: Setting Up SAS. Chapter 16: SAS Programming for the Faint of Heart. Chapter 17: The New World Meets the Old: Programmers and SAS Enterprise Guide. Part VI: The Part of Tens. Chapter 18: Ten SAS Enterprise Guide Productivity Tips. Chapter 19: Ten Tips for Administrators. Chapter 20: Ten (or More) Web Resources for Extra Information. Index.

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Book SynopsisAn accessible introduction to the theoretical and computational aspects of linear algebra using MapleTM Many topics in linear algebra can be computationally intensive, and software programs often serve as important tools for understanding challenging concepts and visualizing the geometric aspects of the subject.Trade Review Table of ContentsPreface. Conventions and Notations. 1 An Introduction To Maple. 1.1 The Commands . 1.2 Programming. 2 Linear Systems of Equations and Matrices. 2.1 Linear Systems of Equations. 2.2 Augmented Matrix of a Linear System and Row Operations. 2.3 Some Matrix Arithmetic. 3 Gauss-Jordan Elimination and Reduced Row Echelon Form. 3.1 Gauss-Jordan Elimination and rref. 3.2 Elementary Matrices. 3.3 Sensitivity of Solutions to Error in the Linear System. 4 Applications of Linear Systems and Matrices. 4.1 Applications of Linear Systems to Geometry. 4.2 Applications of Linear Systems to Curve Fitting. 4.3 Applications of Linear Systems to Economics. 4.4 Applications of Matrix Multiplication to Geometry. 4.5 An Application of Matrix Multiplication to Economics. 5 Determinants, Inverses and Cramer’s Rule. 5.1 Determinants and Inverses from the Adjoint Formula. 5.2 Determinants by Expanding Along Any Row or Column . 5.3 Determinants Found by Triangularizing Matrices. 5.4 LU Factorization. 5.5 Inverses from rref. 5.6 Cramer’s Rule. 6 Basic Linear Algebra Topics. 6.1 Vectors. 6.2 Dot Product. 6.3 Cross Product. 6.4 Vector Projection. 7 A Few Advanced Linear Algebra Topics. 7.1 Rotations in Space. 7.2 ‘Rolling’ a Circle Along a Curve. 7.3 The TNB Frame. 8 Independence, Basis and Dimension for Subspaces of Rn. 8.1 Subspaces of Rn. 8.2 Independent and Dependent Sets of Vectors in Rn. 8.3 Basis and Dimension for Subspaces of Rn. 8.4 Vector Projection onto a Subspace of Rn. 8.5 The Gram-Schmidt Orthonormalization Process. 9 Linear Maps from Rn to Rm. 9.1 Basics About Linear Maps. 9.2 The Kernel and Image Subspaces of a Linear Map. 9.3 Composites of Two Linear maps and Inverses. 9.4 Change of Bases for the Matrix Representation of a Linear Map. 10 The Geometry of Linear and Affine Maps. 10.1 The Effect of a Linear Map on Area and Arclength in Two Dimensions. 10.2 The Decomposition of Linear Maps into Rotations, Reflections and Rescalings in R2. 10.3 The Effect of Linear Maps on Volume, Area and Arclength in R3. 10.4 Rotations, Reflections and Rescalings in Three Dimensions. 10.5 Affine Maps. 11 Least Squares Fits and Pseudoinverses. 11.1 Pseudoinverse to a Non-Square Matrix and Almost Solving an Overdetermined Linear System. 11.2 Fits and Pseudoinverses. 11.3 Least Squares Fits and Pseudoinverses. 12 Eigenvalues and Eigenvectors. 12.1 What Are Eigenvalues and Eigenvectors, and Why Do We Need Them? 12.2 Summary of Definitions and Methods for Computing Eigenvalues and Eigenvectors as well as the Exponential of a Matrix. 12.3 Applications of the Diagonalizability of Square Matrices. 12.4 Solving a Square First Order Linear. System of Differential Equations . . . . . . . . . . . . . . . . . . 12.5 Basic Facts About Eigenvalues and Eigenvectors, and Diagonalizability. 12.6 The Geometry of the Ellipse Using Eigenvalues and Eigenvectors. 12.7 A Maple Eigen-Procedure. Bibliography. Indices. Keyword Index. Index of Maple Commands and Packages.

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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 SynopsisLearn statistical methods quickly and easily with the discovery method With its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote.Trade Review“…the books have plenty of wise advice for the application of statistics…” (Bulletin of Mathematical Biology,2007)Table of ContentsPreface. 1. Variation (or What Statistics Is All About). 2. Probability. 3. Distributions. 4. Testing Hypotheses. 5. Designing an Experiment or Survey. 6. Analyzing Complex Experiments. 7. Developing Models. 8. Reporting Your Findings. 9. Problem Solving. Appendix: An Microsoft Office Excel Primer. Index to Excel and Excel Add-In Functions. Subject 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 Synopsis

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Book Synopsis

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Book SynopsisTrade Review"A thorough, very clearly written overview on the subject of machine learning for those with the prerequisite mathematical tools of calculus, linear algebra and probability."---Jonathan Shock, Mathemafrica"Valuable."---J. Brzezinski, Choice

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Book SynopsisThis textbook provides a self-contained introduction to linear programming using MATLAB software to elucidate the development of algorithms and theory. Early chapters cover linear algebra basics, the simplex method, duality, the solving of large linear problems, sensitivity analysis, and parametric linear programming. In later chapters, the authors discuss quadratic programming, linear complementarity, interior-point methods, and selected applications of linear programming to approximation and classification problems. Exercises are interwoven with the theory presented in each chapter, and two appendices provide additional information on linear algebra, convexity, nonlinear functions, and on available MATLAB commands, respectively. Readers can access MATLAB codes and associated mex files at a Web site maintained by the authors. Only a basic knowledge of linear algebra and calculus is required to understand this textbook, which is geared toward junior and senior-level undergraduate stud

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