Probability and statistics Books

Book SynopsisTrade Review"Data Science for Business and Decision Making brings together the key topics required as the foundation for understanding and applying analytics for decision making. The authors have carefully selected the topics, and each one is clearly explained, described, and reinforced with a diverse set of exercises." --Rahul Saxena, Cobot Systems "Data Science for Business and Decision Making provides a thorough essay about statistical methods which are commonly used in business without requiring a strong mathematical background. The presentation is rigorous and accessible thanks to a large number of examples that are developed step-by-step. The illustrations feature various software and the proposed exercises are particularly helpful for students and practitioners." --Francesco Bartolucci, University of PerugiaTable of ContentsPart 1: Foundations of Business Data Analysis 1. Introduction to Data Analysis and Decision Making 2. Type of Variables and Mensuration Scales Part 2: Descriptive Statistics 3. Univariate Descriptive Statistics 4. Bivariate Descriptive Statistics Part 3: Probabilistic Statistics 5. Introduction of Probability 6. Random Variables and Probability Distributions Part 4: Statistical Inference 7. Sampling 8. Estimation 9. Hypothesis Tests 10. Non-parametric Tests Part 5: Multivariate Exploratory Data Analysis 11. Cluster Analysis 12. Principal Components Analysis and Factorial Analysis Part 6: Generalized Linear Models 13. Simple and Multiple Regression Models 14. Binary and Multinomial Logistics Regression Models 15. Regression Models for Count Data: Poisson and Negative Binomial Part 7: Optimization Models and Simulation 16. Introduction to Optimization Models: Business Problems Formulations and Modeling 17. Solution of Linear Programming Problems 18. Network Programming 19. Integer Programming 20. Simulation and Risk Analysis Part 8: Other Topics 21. Design and Experimental Analysis 22. Statistical Process Control 23. Data Mining and Multilevel Modeling

Read more less

Book SynopsisTable of Contents1. Planning Studies: From Design to Publication 2. Planning Analysis: Addressing Your Scientific Objective 3. Probability and Relative Frequency 4. Distributions 5. Descriptive Statistics 6. Finding Probabilities 7. Hypothesis Testing: Concept and Practice 8. Confidence Intervals 9. Tests on Categorical Data 10. Risks, Odds, and ROC Curves 11. Tests of Location with Continuous Outcomes 12. Equivalence Testing 13. Tests on Variability and Distributions 14. Measuring Association and Agreement 15. Linear Regression and Correlation 16. Multiple Linear and Curvilinear Regression 17. Logistic Regression for Binary Outcomes 18. Regression Models for Count Outcomes 19. Analysis of Censored Time-To-Event Data 20. Analysis of Repeated Continuous Measures of Time 21. Sample Size Estimation 22. Clinical Trials and Group Sequential Analyses 23. Epidemiology and Alternative Sampling Designs 24. Meta Analyses 25. Bayesian Statistics 26. Questionnaires and Surveys 27. Techniques to Aid Analysis 28. Methods You Might Meet, But Not Every Day

Read more less

Book SynopsisTable of ContentsPart I: Introduction and Descriptive Statistics 1. Introduction: Defining the Role of Statistics in Business 2. Data Structures: Classifying the Various Types of Data Sets 3. Histograms: Looking at the Distribution of Data 4. Landmark Summaries: Interpreting Typical Values and Percentiles 5. Variability: Dealing with Diversity Part II: Probability 6. Probability: Understanding Random Situations 7. Random Variables: Working with Uncertain Numbers Part III: Statistical Inference 8. Random Sampling: Planning Ahead for Data Gathering 9. Confidence Intervals: Admitting That Estimates Are Not Exact 10. Hypothesis Testing: Deciding Between Reality and Coincidence Part IV: Regression and Time Series 11. Correlation and Regression: Measuring and Predicting Relationships 12. Multiple Regression: Predicting One Variable From Several Others 13. Report Writing: Communicating the Results of a Multiple Regression 14. Time Series: Understanding Changes Over Time Part V: Methods and Applications 15. ANOVA: Testing for Differences Among Many Samples and Much More 16. Recent Developments 17. Chi-Squared Analysis: Testing for Patterns in Qualitative Data 18. Quality Control: Recognizing and Managing Variation 19. Statistical (Machine) Learning: Using Complex Models With Large Data Sets

Read more less

Book SynopsisTable of Contents1. Introduction 2. A Foundation for Robust Methods 3. Estimating Measures of Location and Scale 4. Confidence Intervals in the One-Sample Case 5. Comparing Two Groups 6. Some Multivariate Methods 7. One-Way and Higher Designs for Independent Groups 8. Comparing Multiple Dependent Groups 9. Correlation and Tests of Independence 10. Robust Regression 11. More Regression Methods 12. ANCOVA

Read more less

Book SynopsisTable of Contents1. An appreciation of baseball and its mathematics 2. Is baseball still the national pastime? 3. Baseball before steroids 4. Bill James and the genesis of sabermetrics 5. Rattling the sabermetrics 6. The annihilation of records: Where have you gone, Babe Ruth? 7. Steroids, etc. 8. Scandal scarred: A discussion of our national pastime’s controversial history 9. The last inning 10. Epilogue: Where have we been? Where do we go from here? A final word from the editor

Read more less

Book SynopsisTable of Contents1. Data and statistics 2. Probability and sampling distributions 3. Principles of inference 4. Inferences on a single population 5. Inferences for two populations 6. Inferences for two or more means 7. Linear regression 8. Multiple regression 9. Linear models 10. Factorial experiments 11. Design of experiments 12. Categorical data 13. Generalized linear models 14. Nonparametric methods

Read more less

Book SynopsisAbout our authors Michael Sullivan, III has training in mathematics, statistics and economics, with a varied teaching background that includes 27 years of instruction in both high school- and college-level mathematics. He is currently a full-time professor of mathematics at Joliet Junior College. Michael has numerous textbooks in publication, including an Introductory Statistics series and a Precalculus series which he writes with his father, Michael Sullivan. Michael believes that his experiences writing texts for college-level math and statistics courses give him a unique perspective on where students are headed once they leave the developmental mathematics tract. This experience is reflected in the philosophy and presentation of his developmental text series. When not in the classroom or writing, Michael enjoys spending time with his 3 children, Michael, Kevin and Marissa, and playing golf. Now that his 2 sons are getting older, he has the opportuTable of ContentsTable of Contents I: GETTING THE INFORMATION YOU NEED Data Collection 1.1 Introduction to the Practice of Statistics 1.2 Observational Studies versus Designed Experiments 1.3 Simple Random Sampling 1.4 Other Effective Sampling Methods 1.5 Bias in Sampling 1.6 The Design of Experiments Chapter Review Chapter Test Making an Informed Decision: What College Should I Attend? Case Study: Chrysalises for Cash II: DESCRIPTIVE STATISTICS Organizing and Summarizing Data 2.1 Organizing Qualitative Data 2.2 Organizing Quantitative Data: The Popular Displays 2.3 Additional Displays of Quantitative Data 2.4 Graphical Misrepresentations of Data Chapter Review Chapter Test Making an Informed Decision: Tables or Graphs? Case Study: The Day the Sky Roared Numerically Summarizing Data 3.1 Measures of Central Tendency 3.2 Measures of Dispersion 3.3 Measures of Central Tendency and Dispersion from Grouped Data 3.4 Measures of Position 3.5 The Five-Number Summary and Boxplots Chapter Review Chapter Test Making an Informed Decision: What Car Should I Buy? Case Study: Who Was “A Mourner”? Describing the Relation Between Two Variables 4.1 Scatter Diagrams and Correlation 4.2 Least-Squares Regression 4.3 Diagnostics on the Least-Squares Regression Line 4.4 Contingency Tables and Association Chapter Review Chapter Test Making an Informed Decision: Relationships Among Variables on a World Scale Case Study: Thomas Malthus, Population, and Subsistence III: PROBABILITY AND PROBABILITY DISTRIBUTIONS Probability 5.1 Probability Rules 5.2 The Addition Rule and Complements 5.3 Independence and the Multiplication Rule 5.4 Conditional Probability and the General Multiplication Rule 5.5 Counting Techniques 5.6 Simulation 5.7 Putting It Together: Which Method Do I Use? Chapter Review Chapter Test Making an Informed Decision: What Are the Effects of Drinking and Driving? Case Study: The Case of the Body in the Bag Discrete Probability Distributions 6.1 Discrete Random Variables 6.2 The Binomial Probability Distribution 6.3 The Poisson Probability Distribution Chapter Review Chapter Test Making an Informed Decision: Should We Convict? Case Study: The Voyage of the St. Andrew The Normal Probability Distribution 7.1 Properties of the Normal Distribution 7.2 Applications of the Normal Distribution 7.3 Assessing Normality 7.4 The Normal Approximation to the Binomial Probability Distribution Chapter Review Chapter Test Making an Informed Decision: What Stock Do I Pick? Case Study: A Tale of Blood, Chemistry, and Health IV: INFERENCE – FROM SAMPLES TO POPULATION Sampling Distributions 8.1 Distribution of the Sample Mean 8.2 Distribution of the Sample Proportion Chapter Review Chapter Test Making an Informed Decision: How Would You Break Down Your Day? Case Study: Sampling Distribution of the Median Estimating the Value of a Parameter Using Confidence Intervals 9.1 Estimating a Population Proportion 9.2 Estimating a Population Mean 9.3 Putting It Together: Which Procedure Do I Use? 9.4 Estimating with Bootstrapping Chapter Review Chapter Test Making an Informed Decision: How Much Should I Spend for this House? Case Study: When Model Requirements Fail Hypothesis Tests Regarding a Parameter 10.1 The Language of Hypothesis Testing 10.2A Hypothesis Tests on a Population Proportion with Simulation 10.2B Hypothesis Tests on a Population Proportion Using the Normal Model 10.3A Using Simulation/Bootstrapping in Hypothesis Tests for a Population Mean 10.3B Hypothesis Tests for a Population Mean 10.4 Putting It Together: Which Procedure Do I Use? Chapter Review Chapter Test Making an Informed Decision: Selecting a Mutual Fund Case Study: How Old Is Stonehenge? Inference on Two Samples 11.1A Using Randomization Techniques to Compare Two Proportions 11.1 Inference about Two Population Proportions: Independent Samples 11.2A Using Bootstrapping to Conduct Inference on Two Dependent Means 11.2 Inference about Two Population Means: Dependent Samples 11.3A Using Randomization Techniques to Compare Two Independent Means 11.3 Inference about Two Population Means: Independent Samples 11.4 Putting It Together: Which Procedure Do I Use? Chapter Review Chapter Test Making an Informed Decision: Which Car Should I Buy? Case Study: Control in the Design of Experiment Inference on Categorical Data 12.1 Goodness-of-Fit Test 12.2 Tests for Independence and the Homogeneity of Proportions 12.3 Inference about Two Population Proportions: Dependent Samples Chapter Review Chapter Test Making an Informed Decision: What Are the Benefits of College? Case Study: Feeling Lucky? Well, Are You? Comparing Three or More Means 13.1 Comparing Three or More Means: One-Way Analysis of Variance 13.2 Post-Hoc Tests on One-Way Analysis of Variance Chapter Review Chapter Test Making an Informed Decision: Where Should I Invest? Case Study: Hat Size and Intelligence Inference of the Least-Squares Regression Model 14.1A Using Randomization Techniques on the Slope of the Least-Squares Regression Line 14.1 Testing the Significance of the Least-Squares Regression Model 14.2 Confidence and Prediction Intervals Chapter Review Chapter Test Making an Informed Decision: Buying a Home Case Study: Housing Boom Appendix A: Tables Appendix B: Lines Appendix C: Additional Topics C.1 The Normal Approximation to the Binomial Probability Distribution C.2 Estimating a Population Standard Deviation C.3 Hypothesis Tests for a Population Standard Deviation

Read more less

Book SynopsisJ. MURDOCH was formerly Director of Management Science Studies at Cranfield University.J.A. BARNES was formerly Senior Lecturer in Management Science Studies at Cranfield University.

Read more less

Book SynopsisBasics.- The R environment.- Probability and distributions.- Descriptive statistics and graphics.- One- and two-sample tests.- Regression and correlation.- Analysis of variance and the KruskalWallis test.- Tabular data.- Power and the computation of sample size.- Advanced data handling.- Multiple regression.- Linear models.- Logistic regression.- Survival analysis.- Rates and Poisson regression.- Nonlinear curve fitting.Trade ReviewFrom the reviews:TECHNOMETRICS"…extensive, well organized, and well documented…The book is an elegant R companion, suitable for the statistically initiated who want to program their own analyses. For experienced statisticians and data analysts, the book provides a good overview of the basic statistical analysis capabilities of R and presumably prepares readers for later migration to S…The format of this compact book is attractive…The book makes excellent use of fonts and intersperses graphics near the codes that produced them. Output from each procedure is dissected line by line to link R code with the computed result…I can recommend [this book] to its target audience. The author provides an excellent overview of R. I found the wealth of clear examples educational and a practical way to preview both R and S.""The scope of the book, introductory statistics, is a very useful set of methods in parametric and non-parametric statistics up to logistic regression and survival analysis. … Where many constructs in R are very attractive for mathematical oriented users, e.g. matrices, Dalgaard succeeded in convincing me that with little extra effort they can be made very useful to less mathematically oriented people, e.g. by specifying row and column names, and proposing quite attractive ways to specify for example ‘subsets’ of rows and columns." (Dr. H. W. M. Hendriks, Kwantitatieve Methoden, Vol. 72B8, 2003)"R is an Open Source implementation of the well-known S language. It works on multiple computing platforms and can be freely downloaded. R is thus ideally suited for teaching at many levels as well as for practical data analysis and methodological development. This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. … Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets." (Zentralblatt für Didaktik der Mathematik, August, 2004)"This is a nice book on statistical methods and statistical computing in R, a language and environment for statistical computing and graphs: this dialect of the S language is available as free software through internet. … Explanation of statistical methods, together with an interpretation of statistical concepts, is the prevailing style of the text. They are illustrated by plenty of practical examples, all computed using R. This book will be useful for novices in applied statistics or in computing in R." (European Mathematical Society Newsletter, September, 2003)"The book is an elegant R companion, suitable for the statistically initiated who want to program their own analyses. For experienced statisticians and data analysts, the book provides a good overview of the basic statistical analysis capabilities of R … prepares readers for later migration to S. … I can recommend Introductory Statistics With R to its target audience. The author provides an excellent overview of R. I found the wealth of clear examples educational and a practical way to preview both R and S." (Thomas D. Sandry, Technometrics, Vol. 45 (3), 2003)"R is both a statistical computer environment and a programming language designed to perform statistical analysis and to produce adequate corresponding graphics. … The present book is … a very useful guide for introducing a number of basic concepts and techniques necessary to practical statistics, covering both elementary statistics and actual programming in the R language. The book is organized in 12 chapters and three appendices, each chapter ending with a beneficial section of proposed exercises." (Silvia Curteanu, Zentralblatt MATH, Vol. 1006, 2003)From the reviews of the second edition:“This review … roughly cover the introductory topics of a first year statistics course. The Introductory Statistics with R (ISwR) book is targeted for a biometric/medical audience. It covers more topics … like multiple regression and survival analysis and expects the reader to know about basic statistics. … include examples and graphs together with the R code to construct them. … The ISwR book is good for an academic and biometric audience.” (Wolfgang Polasek, Statistical Papers, Vol. 52, 2011)“This is a welcome addition to the new edition that will be appreciated by its users. … The new edition is well written, and the new materials are well incorporated. Like the first edition, this edition will continue to be useful to the target audience, and I can safely recommend it to them.” (Technometrics, Vol. 51 (2), May, 2009)Table of ContentsBasics. - The R environment. - Probability and statistics. - Descriptive statistics and graphics. - One and two sample tests. - Regression and correlation. - ANOVA and Kruskal-Wallis. - Tabular data. - Power and the computation of sample size. - Advanced data handling. - Multiple regression. - Linear models. - Logistic regression. - Survival analysis. - Rates and Poisson regression. - Nonlinear curve-fitting. - Obtaining and installing R and the ISwR package. - Data sets in the ISwR package. - Compendium. - Answers to exercises. - Index.

Read more less

Book SynopsisIn politics, you begin by asking theoretically interesting questions. Sometimes statistics can help answer those questions. When it comes to applied statistics, students shouldnât just learn a vast array of formulaâthey need to learn the basic concepts of statistics as solutions to particular problems. Peter Galderisi demonstrates that statistics are a summary of how to answer the problem: learn the math but only after learning the concepts and methodological considerations that give it context. With this as a starting point, Understanding Political Science Statistics asks students to consider how to address a research problem conceptually before being led to the appropriate formula. Throughout, Galderisi looks at problems through a lens of observations and expectations, which can be applied to myriad statistical techniques, both descriptive and inferential. This approach links the answers researchers get from their individual data analysis to the research designs and Trade Review“The strength of this text is that Galderisi very carefully walks students through the statistical exercises and, in many cases, provides visual aids to help demonstrate the patterns or conclusions. He also works hard to take a step back from the statistics to help students understand the meaning of the analysis and why statistical conclusions may be invalid. This is not always included in many texts, which have more of a ‘how-to’ approach, while the approach here is very thoughtful.” – Daniel Coffey, University of Akron“Understanding Political Science Statistics is an indispensable text that gives students the tools to understand how to think about and analyze political phenomenon. Galderisi’s applied, problem-focused approach to thinking about hypothesis testing and quantitative analysis ensure that it is both comprehensive and accessible to a broad range of readers with varying backgrounds and mathematical acumen.” – Vladimir Kogan, Ohio State University“Most textbooks in this field start with statistics, and then tack on political examples in aid of the math. The starting point for Peter Galderisi’s well-crafted new book is learning about politics, with the formulas and data always working to answer substantively important questions about the political world. I’ve assigned it in my classes, and seen firsthand how engaged students become with the concepts and tools of quantitative analysis when a text piques and satisfies their curiosity about politics. Understanding Political Science Statistics does this, without sacrificing rigor in any way.” – Thad Kousser, UC San Diego “Understanding Political Science Statistics presents introductory statistics for political science students in an engaging, pragmatic, and problem-centric fashion that will be very approachable to undergraduates. Instead of an overly formal mathematical presentation, Galderisi explores research design and statistical concepts intuitively, showing students who may not be initially interested in the material how quantitative data and analysis can help them better understand contemporary issues in political science and public policy.” – Jack Reilly, New College of Florida“This is an ideal book for an undergraduate research methods course in political science. Galderisi offers a real treat for both students and instructors of statistics and research methods in political science. His work is thoroughly readable and at the same time provides a broad overview of the tools that everyone needs to know to be an informed consumer—and potentially a future producer—of quantitative research in political science.” – Mack Shelley, Iowa State UniversityTable of Contents1. Political Science, the Scientific Method, and Statistical Analysis: An Overview2. How Do We Measure and Observe?3. Central Tendency as Summary Observation4. Dispersion, Variation, Goodness of Fit as Summary Observation 5. Standardized Scores and Normal Distributions: The Concept of Relative Observation 6. An Intuitive Introduction to Inference and Hypothesis Testing7. Hypothesis Testing and the Concept of ASsociation: Observations and Expectatiosn about the Difference between Two Means 8. Inferential Statistics for Proportions9. Measuring Association for Nominal and Ordinal Data 10. Research Design and the Use of Control Variables 11. Different by How Much? Linear Regression12. Retracing Our Steps: Hypotheses, Multiple Regression, and the EFfects of Third Variables

Read more less

Book SynopsisTable of Contents1. Statistics, Experiments, and Data 2. Probability 3. Probability Distributions I: Basic Concepts 4. Probability Distributions II: Examples 5. Sampling and Estimation 6. Sampling Distributions Associated with the Normal Distribution 7. Parameter Estimation I: Maximum Likelihood and Minimum Variance 8. Parameter Estimation II: Least-Squares and Other Methods 9. Interval Estimation 10. Hypothesis Testing I: Parameters 11. Hypothesis Testing II: Other Tests Appendices 1. Miscellaneous Mathematics 2. Optimization of Nonlinear Functions 3. Statistical Tables 4. Answers to Selected Problems

Read more less

Book SynopsisThe revealing and tremendously entertaining look at how luck really worksTrade Review'Another delightful addition to the stuff-you-think-you-know-that's-wrong genre, á la Freakonomics, Outliers, and The Black Swan' Kirkus (starred review)

Read more less

Book SynopsisThis book in the Edinburgh Textbooks in Empirical Linguistics series is a comprehensive introduction to the statistics currently used in corpus linguistics.Trade ReviewGives many instructive examples of statistical techniques applied to language data, provides exercises at the end of each chapter, and has a clear glossary. Year's Work in English Studies This book is essential for learning statistical analysis of texts. Most importantly, Oakes's is the only current corpus-linguistic book that devotes such a large portion of its pages to stylistics ! the whole book is worthwhile for the literary scholar. Language and Literature Gives many instructive examples of statistical techniques applied to language data, provides exercises at the end of each chapter, and has a clear glossary. This book is essential for learning statistical analysis of texts. Most importantly, Oakes's is the only current corpus-linguistic book that devotes such a large portion of its pages to stylistics ! the whole book is worthwhile for the literary scholar.

Read more less

Book SynopsisStatistical distributions are one of the most important applied mathematical tools across a wide spectrum of disciplines, including engineering, biological sciences, and health and social sciences. Since they are used to model observed data and ultimately to develop inferential procedures, understanding the properties of statistical distributions is critical to developing optimal inferential methods and validating the resulting model assumptions. Advances on Models, Characterizations and Applications offers up-to-date information on many recent developments in the field.Comprising fourteen self-contained chapters contributed by internationally renowned experts, this book delineates recent developments on characterizations and other important properties of several distributions, inferential issues related to these models, and several applications of the models to real-world problems. Each chapter is rich with references for further study or more in-depth information on each topiTable of ContentsThe Shapes of the Probability Density, Hazard, and Reverse Hazard Functions. Stochastic Ordering of Risks, Influence of Dependence, and A.S. Constructions. The q-Factorial Moments of Discrete q-Distributions and a Characterization of the Euler Distribution. On the Characterization of Distributions Through the Properties of Conditional Expectations of Order Statistics. Characterization of the Exponential Distribution by Conditional Expectations of Generalized Spacings. Some Characterizations of Exponential Distribution Based on Progressively Censored Order Statistics. A Note on Regressing Order Statistics and Record Values. Generalized Pareto Distributions and Their Characterizations. On Characteristic Properties of the Uniform Distribution. Characterizations of Multivariate Distributions Involving Conditional Specification and/or Hidden Truncation. Bivariate Matsumoto-Yor Property and Related Characterizations. First Principal Component Characterization of a Continuous Random Variable. The Lawless-Wang's Operational Ridge Regression Estimator Under the Linex Loss Function. On the Distributions of the Reference Dose and Its Application in Health Risk Assessment. Subject Index.

Read more less

Book SynopsisShowcasing a discussion of the experimental process and a review of basic statistics, this volume provides methodologies to identify general data distribution, skewness, and outliers. It features a unique classification of the nonparametric analogs of their parametric counterparts according to the strength of the collected data. Applied Statistical Designs for the Researcher discusses three varieties of the Student t test, including a comparison of two different groups with different variances; two groups with the same variance; and a matched, paired group. It introduces the analysis of variance and Latin Square designs and presents screening approaches to comparing two factors and their interactions.Table of ContentsResearch and Statistics Basic Review of Parametric Statistics Exploratory Data Analysis Two Sample Tests Completely Randomized One-Factor Analysis of Variance One and Two Restrictions on Randomization Completely Randomized Two-Factor Factorial Designs Two-Factor Factorial Completely Randomized Blocked Designs Useful Small Scale Pilot Designs Nested Statistical Designs Linear Regression Nonparametric Statistics Introduction to Research Synthesis and "Meta-Analysis" and Conclusory Remarks References Index.

Read more less

Book SynopsisA technically precise yet clear presentation of modern sequential methodologies having immediate applications to practical problems in the real world, Applied Sequential Methodologies communicates invaluable techniques for data mining, agricultural science, genetics, computer simulation, finance, clinical trials, sonar signal detection, randomization, multiple comparisons, psychology, tracking, surveillance, and numerous additional areas of application.Includes more than 500 references, 165 figures and tables, and over 25 pages of subject and author indexes.Applied Sequential Methodologies brings the crucial nature of sequential approaches up to speed with recent theoretical gains, demonstrating their utility for solving real-life problems associated with Change-point detection in multichannel and distributed systems Best component selection for multivariate distributions Multistate processes Approximations for moving sums of discrete random variables Interim and terminal analyses of clinical trials Adaptive designs for longitudinal clinical trials Slope estimation in measurement-error models Tests for randomization and target tracking Appropriate count of simulation runs Stock price models Orders of genes Size and power control in multiple comparisonsAuthored by 33 leading scientists, this volume will greatly benefit sequential analysts, data analysts, applied statisticians, biometricians, clinical trialists, and upper-level undergraduate and graduate students in these disciplines. Table of ContentsPassive Acoustic Detection of Marine Mammals Using Page's Test. Two-Stage Procedures for Selecting the Best Component of a Multivariate Distribution. Sequential Randomization Tests. Sequential Methods for Multistate Processes. Sequential Adaptive Designs for Clinical Trials with Longitudinal Responses. Sequential Approaches to Data Mining. Approximations and Bounds for Moving Sums of Discrete Random Variables. Estimation of the Slope in a Measurement-Error Model. Kernel Density Estimation of Wool Fiber Diameter. Financial Applications of Sequential Nonparametric Curve Estimation.

Read more less

Book SynopsisSurfaces are a central to geographical analysis. Their generation and manipulation are a key component of geographical information systems (GISs). However, geographical surface data is often not precise. When surfaces are used to model geographical entities, the data inherently contains uncertainty in terms of both position and attribute. Fuzzy Surface in GIS and Geographical Analysis sets out a process to identify the uncertainty in geographic entities. It describes how to successfully obtain, model, analyze, and display data, as well as interpret results within the context of GIS. Focusing on uncertainty that arises from transitional boundaries, the book limits its study to three types of uncertainties: intervals, fuzzy sets, and possibility distributions. The book explains that uncertainty in geographical data typically stems from these three and it is only natural to incorporate them into the analysis and display of surface data. The book defines the mathematics associatTable of ContentsIntroduction. Interpolation with Data Containing Interval, Fuzzy, and Possibilistic Uncertainty. Introduction to Geographical Information Systems. Geographical Entities as Surfaces.Surface Modeling. Visualization and Analysis on Surfaces. Applications. Algorithms - Pseudo Code. Appendices –Fuzzy Arithmetic and Fuzzy Query C++ Source.

Read more less

Book SynopsisThe formal description of non-precise data before their statistical analysis is, except for error models and interval arithmetic, a relatively young topic. Fuzziness is described in the theory of fuzzy sets but only a few papers on statistical inference for non-precise data exist. In many cases, for example when very small concentrations are being measured, it is necessary to describe the imprecision of data. Otherwise, the results of statistical analysis can be unrealistic and misleading. Fortunately, there is a straightforward technique for dealing with non-precise data. The technique - the generalized inference method - is explained in Statistical Methods for Non-Precise Data. Anyone who understands elementary statistical methods and simple stochastic models will be able to use this book to understand and work with non-precise data.The book includes explanations of how to cope with non-precise data in different practical situations, and makes an excellent graduate level text booTable of ContentsNon-Precise Data and Their Formal DescriptionNon-Precise DataNon-Precise Numbers and Characterizing FunctionsConstruction of Characterizing FunctionsNon-Precise VectorsFunctions of Non-Precise Quantities and Non-Precise FunctionsDescriptive Statistics with Non-Precise DataNon-Precise SamplesHistograms for Non-Precise DataCumulative Sums for Non-Precise DataEmpirical Distribution Function for Non-Precise DataEmpirical Fractiles for Non-Precise DataFoundations for Statistical Inference with Non-Precise DataCombination of Non-Precise ObservationsSample Moment for Non-Precise ObservationsSequences of Non-Precise ObservationsClassical Statistical Inference for Non-Precise DataPoint Estimators for ParametersConfidence Regions for ParametersNonparametric EstimationStatistical Tests and Non-Precise DataBayesian Inference for Non-Precise DataBayes' Theorem for Non-Precise DataBayesian Confidence Regions Based on Non-Precise DataNon-Precise Predictive DistributionsNon-Precise a priori DistributionsBayes Theorem for Non-Precise a priori Distribution and Non-Precise DataBayesian Decisions Based on Non-Precise InformationOutlookReferencesList of SymbolsIndex

Read more less

Book SynopsisCRC Handbook of Percentiles of Non-Central t-Distributions is the first book to provide critical values of non-central t-distributions in an easy-to-use format. The book presents a brief introductory section that outlines properties and applications of non-central t-distributions and then explains the tables. The rest of the book consists of tables. The values listed were produced using cumulative probabilities. CRC Handbook of Percentiles of Non-Central t-Distributions is an essential reference of numerical tables of statistical functions for researchers, practitioners, scientists, and students involved with statistics in the areas of tolerance limits, variable sampling plans, confidence limits on quantiles, confidence limits on proportions, sample coefficient of variation and in computing the power of t-test.Table of ContentsIntroduction: The Critical Values of Non-Central t Distributions. a=0.01, a=0.025, a=0.05, a=0.10, a=0.20, a=0.30, a=0.70, a=0.80, a=0.90, a=0.95, a=0.975, a=0.99. References.

Read more less

Book SynopsisCovering basic univariate and bivariate statistics and regression models for nominal, ordinal, and interval outcomes, Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with fundamental skills to estimate, interpret, and publish quantitative research using contemporary standards.Reflecting the growing importance of Big Data in the social and health sciences, this thoroughly revised and streamlined new edition covers best practice in the use of statistics in social and health sciences, draws upon new literatures and empirical examples, and highlights the importance of statistical programming, including coding, reproducibility, transparency, and open science.Key features of the book include: interweaving the teaching of statistical concepts with examples from publicly available social and health science data and literature excerpts; thoroughly integrating the teaching of statistiTrade Review"This book is a teacher’s dream. Not only does it provide a comprehensive discussion of statistics as it is actually practiced by working researchers in the social and health sciences, it also provides detailed guidance on how to carry out such analyses using Stata, one of the best available and widely used statistical packages. Finally it provides numerous examples drawn directly from the research literature. I know of no other book like it."Richard Campbell, Emeritus Professor of Public Health, University of Illinois at Chicago, USA "I taught a year-long graduate level statistics course to first year sociology, education, policy analysis and demography Ph.D. students for more than 40 years. I always pieced together material from several different textbooks, software manuals, and published articles, since no one volume met the need to provide entering graduate students with appropriate content coverage at the right difficulty level. The 2nd edition of Rachel Gordon’s book, with its excellent update, meets these needs better than any other volume I have seen."George Farkas, Distinguished Emeritus Professor of Education, University of California, Irvine, USA "I have used the first edition of Rachel Gordon’s Applied Statistics for the Social and Health Sciences in my multidisciplinary graduate-level statistics course since I began teaching it around 5 years ago. Gordon’s ability to translate complex information into practical, real-world examples that are applicable and engaging for students across the social sciences and health disciplines has helped her textbook stand out from others. The second edition enhances this even further, bringing the material fully up-to-date with recent advances, and displaying a much-needed focus on developing students’ coding skills as well as their statistical knowledge. I anticipate Gordon’s second edition becoming a standard textbook in the field for years to come."Jeffrey E. Stokes, Assistant Professor of Gerontology & Undergraduate Director of Aging Studies Program, University of Massachusetts Boston, USA "I have used—and loved—the first edition of this book for nearly a decade. However, I was thrilled to see that the new edition promises to retain the rigor and clarity of purpose of the first edition, but in a more focused and streamlined package. I look forward to adopting this book for my introductory and advanced regression courses for the next decade and beyond."Jeffrey M. Timberlake, Professor of Sociology, University of Cincinnati, USA Table of ContentsPart I: Getting ready; 1 Considering Examples of Scholarly Publications Modeling Social and Health Variables; 2 Planning and Starting a Quantitative Research Project with Existing Data;; Part II: Describing the data; 3 Graphing and Summarizing Individual Variables; 4 Introducing Population Estimation and Hypothesis Testing; 5 Estimating and Testing the Association between Two Variables; ; Part III: Estimating and presenting linear regression models; 6 Introducing the Linear Regression Model with Two Continuous Variables; 7 Considering Nonlinearity and Nonconstant Variance; 8 Including Categorical Predictor Variables; 9 Including More Than One Predictor Variable in the Model; 10 Considering Interactions among Predictor Variables; ; Part IV: Estimating and presenting generalized linear models; 11 Introducing the Generalized Linear Regression Model; 12 Analyzing Dichotomous Outcomes; 13 Analyzing Multi-Category Outcomes and Offering a Roadmap to Additional Models

Read more less

Book SynopsisPraise for the Second Edition:The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and could work well as a required text for an advanced undergraduate or graduate course. ---Computing ReviewsProbability and Statistics for Computer Scientists, Third Edition helps students understand fundamental concepts of Probability and Statistics, general methods of stochastic modeling, simulation, queuing, and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB, this classroom-tesTable of Contents 1. Introduction and Overview 2. Probability 3. Discrete Random Variables and Their Distributions 4. Continuous Distributions 5. Computer Simulations and Monte Carlo Methods 6. Stochastic Processes 7. Queuing Systems 8. Introduction to Statistics 9. Statistical Inference I 10. Statistical Inference II 11. Regression 12. Appendix

Read more less

Book SynopsisA Practical Guide to Managing Clinical Trials is a basic, comprehensive guide to conducting clinical trials. Designed for individuals working in research site operations, this user-friendly reference guides the reader through each step of the clinical trial process from site selection, to site set-up, subject recruitment, study visits, and to study close-out. Topics include staff roles/responsibilities/training, budget and contract review and management, subject study visits, data and document management, event reporting, research ethics, audits and inspections, consent processes, IRB, FDA regulations, and good clinical practices. Each chapter concludes with a review of key points and knowledge application.Unique to this book is A View from India, a chapter-by-chapter comparison of clinical trial practices in India versus the U.S. Throughout the book and in Chapter 10, readers will glimpse some of the challenges and opportunities in the emerging and Trade Review'"A Practical Guide to Managing Clinical Trials" provides a good introduction to the basics of clinical research for investigators, study coordinators, and other site personnel. The clear writing makes the book a quick read.' – Norman M. Goldfarb for Journal of Clinical Research Best Practices, Vol. 13, No. 12, December 2017.'"A Practical Guide to Managing Clinical Trials" provides a good introduction to the basics of clinical research for investigators, study coordinators, and other site personnel. The clear writing makes the book a quick read.' – Norman M. Goldfarb for Journal of Clinical Research Best Practices, Vol. 13, No. 12, December 2017.Table of ContentsChapter 1: Rules, Roles and ResponsibilitiesChapter 2: Products, Protocols, and Pre-trial PreparationChapter 3: Sponsor, Site and Study Start-upChapter 4: Enticement, Enrollment, and Engagement: The Informed Consent ProcessChapter 5: From Enrollment to Final VisitChapter 6: Collaborating for Compliance and Quality Data – Monitoring and AuditsChapter 7: Building BudgetsChapter 8: Contracts, Clauses and Closing the DealChapter 9: US Clinical Trials – Additional TopicsChapter 10: Clinical Research and India

Read more less

Book Synopsis

Read more less