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Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests: A Computational Approach describes classical nonparametric tests as well as novel and little-known methods such as the Baumgartner-Weiss-Schindler and the Cucconi tests. The book presents SAS and R programs allowing readers to carry out the different statistical methods such as permutation and bootstrap tests. The author considers example data sets in each chapter to illustrate methods. Numerous real-life data from various areas including the bible and their analyses provide for greatly diversified reading. The book covers: Nonparametric two-sample tests for the location-shift model specifically the Fisher-Pitman permutation test the Wilcoxon rank sum test and the Baumgartner-Weiss-Schindler test Permutation tests location-scale tests tests for the nonparametric Behrens-Fisher problem and tests for a difference in variability Tests for the general alternative including the (Kolmogorov-)Smirnov test ordered categorical and discrete numerical data Well-known one-sample tests such as the sign test and Wilcoxon’s signed rank test a modification suggested by Pratt (1959) a permutation test with original observations and a one-sample bootstrap test are presented. Tests for more than two groups the following tests are described in detail: the Kruskal-Wallis test the permutation F test the Jonckheere-Terpstra trend test tests for umbrella alternatives and the Friedman and Page tests for multiple dependent groups The concepts of independence and correlation and stratified tests such as the van Elteren test and combination tests The applicability of computer-intensive methods such as bootstrap and permutation tests for non-standard situations and complex designs Although the major development of nonparametric methods came to a certain end in the 1970s their importance undoubtedly persists. What is still needed is a computer assisted evaluation of their main properties. This book closes that gap. | Nonparametric Statistical Tests A Computational Approach

GBP 69.99
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Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

In engineering and quality control various situations including process validation and design verification require equivalence and noninferiority tests. Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers presents methods for using validation and verification test data to demonstrate equivalence and noninferiority in engineering and applied science. The book covers numerous tests drawn from the author’s more than 30 years of work in a range of industrial settings. It provides computational formulas for the tests methods to determine or justify sample sizes and formulas to calculate power and operating characteristic curves. The methods are accessible using standard statistical software and do not require complicated programming. The book also includes computer code and screen shots for SAS R and JMP. This book provides you with a guide to performing validation and verification tests that demonstrate the adequacy of your process system or product. It will help you choose the best test for your application.

GBP 59.99
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Basic Statistics and Pharmaceutical Statistical Applications

Basic Statistics and Pharmaceutical Statistical Applications

Building on its best-selling predecessors Basic Statistics and Pharmaceutical Statistical Applications Third Edition covers statistical topics most relevant to those in the pharmaceutical industry and pharmacy practice. It focuses on the fundamentals required to understand descriptive and inferential statistics for problem solving. Incorporating new material in virtually every chapter this third edition now provides information on software applications to assist with evaluating data. New to the Third EditionUse of Excel® and Minitab® for performing statistical analysisDiscussions of nonprobability sampling procedures determining if data is normally distributed evaluation of covariances and testing for precision equivalenceExpanded sections on regression analysis chi square tests tests for trends with ordinal data and tests related to survival statistics Additional nonparametric procedures including the one-sided sign test Wilcoxon signed-ranks test and Mood’s median testWith the help of flow charts and tables the author dispels some of the anxiety associated with using basic statistical tests in the pharmacy profession and helps readers correctly interpret their results using statistical software. Through the text’s worked-out examples readers better understand how the mathematics works the logic behind many of the equations and the tests’ outcomes.

GBP 44.99
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A Pen and Paper Introduction to Statistics

A Pen and Paper Introduction to Statistics

Statistics is central in the biosciences social sciences and other disciplines yet many students often struggle to learn how to perform statistical tests and to understand how and why statistical tests work. Although there are many approaches to teaching statistics a common framework exists between them: starting with probability and distributions then sampling from distribution and descriptive statistics and later introducing both simple and complex statistical tests typically ending with regression analysis (linear models). This book proposes to reverse the way statistics is taught by starting with the introduction of linear models. Today many statisticians know that the one unifying principle of statistical tests is that most of them are instances of linear models. This teaching method has two advantages: all statistical tests in a course can be presented under the same unifying framework simplifying things; second linear models can be expressed as lines over squared paper replacing any equation with a drawing. This book explains how and why statistics works without using a single equation just lines and squares over grid paper. The reader will have the opportunity to work through the examples and compute sums of squares by just drawing and counting and finally evaluating whether observed differences are statistically significant by using the tables provided. Intended for students scientists and those with little prior knowledge of statistics this book is for all with simple and clear examples computations and drawings helping the reader to not only do statistical tests but also understand statistics. | A Pen and Paper Introduction to Statistics

GBP 31.99
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An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented but robust techniques are considered as well. These techniques include one-sample testing and estimation multi-sample testing and estimation and regression. Attention is paid to the intellectual development of the field with a thorough review of bibliographical references. Computational tools in R and SAS are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign Kruskal-Wallis Friedman Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology elementary probability and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration and ideally a course in matrix algebra.

GBP 84.99
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Statistical Thinking in Clinical Trials

Statistical Thinking in Clinical Trials

Statistical Thinking in Clinical Trials combines a relatively small number of key statistical principles and several instructive clinical trials to gently guide the reader through the statistical thinking needed in clinical trials. Randomization is the cornerstone of clinical trials and randomization-based inference is the cornerstone of this book. Read this book to learn the elegance and simplicity of re-randomization tests as the basis for statistical inference (the analyze as you randomize principle) and see how re-randomization tests can save a trial that required an unplanned mid-course design change. Other principles enable the reader to quickly and confidently check calculations without relying on computer programs. The `EZ’ principle says that a single sample size formula can be applied to a multitude of statistical tests. The `O minus E except after V’ principle provides a simple estimator of the log odds ratio that is ideally suited for stratified analysis with a binary outcome. The same principle can be used to estimate the log hazard ratio and facilitate stratified analysis in a survival setting. Learn these and other simple techniques that will make you an invaluable clinical trial statistician.

GBP 82.99
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Handbook of Statistical Distributions with Applications

Handbook of Statistical Distributions with Applications

Easy-to-Use Reference and Software for Statistical Modeling and TestingHandbook of Statistical Distributions with Applications Second Edition provides quick access to common and specialized probability distributions for modeling practical problems and performing statistical calculations. Along with many new examples and results this edition includes both the author’s StatCalc software and R codes to accurately and easily carry out computations. New to the Second Edition Major changes in binomial Poisson normal gamma Weibull exponential logistic Laplace and Pareto distributions Updated statistical tests and intervals based on recent publications in statistical journals Enhanced PC calculator StatCalc with electronic help manualsR functions for cases where StatCalc is not applicable with the codes available onlineThis highly praised handbook integrates popular probability distribution models formulas applications and software to help you compute a variety of statistical intervals. It covers probability and percentiles algorithms for random number generation hypothesis tests confidence intervals tolerance intervals prediction intervals sample size determination and much more.

GBP 44.99
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Analytical Similarity Assessment in Biosimilar Product Development

Analytical Similarity Assessment in Biosimilar Product Development

This book focuses on analytical similarity assessment in biosimilar product development following the FDA’s recommended stepwise approach for obtaining totality-of-the-evidence for approval of biosimilar products. It covers concepts such as the tiered approach for assessment of similarity of critical quality attributes in the manufacturing process of biosimilar products models/methods like the statistical model for classification of critical quality attributes equivalence tests for critical quality attributes in Tier 1 and the corresponding sample size requirements current issues and recent developments in analytical similarity assessment.

GBP 48.99
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Real Analysis and Foundations

Real Analysis and Foundations

Through four editions this popular textbook attracted a loyal readership and widespread use. Students find the book to be concise accessible and complete. Instructors find the book to be clear authoritative and dependable. The primary goal of this new edition remains the same as in previous editions. It is to make real analysis relevant and accessible to a broad audience of students with diverse backgrounds while also maintaining the integrity of the course. This text aims to be the generational touchstone for the subject and the go-to text for developing young scientists. This new edition continues the effort to make the book accessible to a broader audience. Many students who take a real analysis course do not have the ideal background. The new edition offers chapters on background material like set theory logic and methods of proof. The more advanced material in the book is made more apparent. This new edition offers a new chapter on metric spaces and their applications. Metric spaces are important in many parts of the mathematical sciences including data mining web searching and classification of images. The author also revised the material on sequences and series adding examples and exercises that compare convergence tests and give additional tests. The text includes rare topics such as wavelets and applications to differential equations. The level of difficulty moves slowly becoming more sophisticated in later chapters. Students have commented on the progression as a favorite aspect of the textbook. The author is perhaps the most prolific expositor of upper division mathematics. With over seventy books in print thousands of students have been taught and learned from his books. | Real Analysis and Foundations

GBP 82.99
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Essentials of Probability Theory for Statisticians

Essentials of Probability Theory for Statisticians

Essentials of Probability Theory for Statisticians provides graduate students with a rigorous treatment of probability theory with an emphasis on results central to theoretical statistics. It presents classical probability theory motivated with illustrative examples in biostatistics such as outlier tests monitoring clinical trials and using adaptive methods to make design changes based on accumulating data. The authors explain different methods of proofs and show how they are useful for establishing classic probability results. After building a foundation in probability the text intersperses examples that make seemingly esoteric mathematical constructs more intuitive. These examples elucidate essential elements in definitions and conditions in theorems. In addition counterexamples further clarify nuances in meaning and expose common fallacies in logic. This text encourages students in statistics and biostatistics to think carefully about probability. It gives them the rigorous foundation necessary to provide valid proofs and avoid paradoxes and nonsensical conclusions.

GBP 56.99
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A Primer on Linear Models

A Primer on Linear Models

A Primer on Linear Models presents a unified thorough and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact finite sample theory supporting common statistical methods. With coverage steadily progressing in complexity the text first provides examples of the general linear model including multiple regression models one-way ANOVA mixed-effects models and time series models. It then introduces the basic algebra and geometry of the linear least squares problem before delving into estimability and the Gauss Markov model. After presenting the statistical tools of hypothesis tests and confidence intervals the author analyzes mixed models such as two-way mixed ANOVA and the multivariate linear model. The appendices review linear algebra fundamentals and results as well as Lagrange multipliers. This book enables complete comprehension of the material by taking a general unifying approach to the theory fundamentals and exact results of linear models.

GBP 175.00
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C++ for Financial Mathematics

C++ for Financial Mathematics

If you know a little bit about financial mathematics but don’t yet know a lot about programming then C++ for Financial Mathematics is for you. C++ is an essential skill for many jobs in quantitative finance but learning it can be a daunting prospect. This book gathers together everything you need to know to price derivatives in C++ without unnecessary complexities or technicalities. It leads the reader step-by-step from programming novice to writing a sophisticated and flexible financial mathematics library. At every step each new idea is motivated and illustrated with concrete financial examples. As employers understand there is more to programming than knowing a computer language. As well as covering the core language features of C++ this book teaches the skills needed to write truly high quality software. These include topics such as unit tests debugging design patterns and data structures. The book teaches everything you need to know to solve realistic financial problems in C++. It can be used for self-study or as a textbook for an advanced undergraduate or master’s level course.

GBP 44.99
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The Weibull Distribution A Handbook

The Weibull Distribution A Handbook

The Most Comprehensive Book on the SubjectChronicles the Development of the Weibull Distribution in Statistical Theory and Applied StatisticsExploring one of the most important distributions in statistics The Weibull Distribution: A Handbook focuses on its origin statistical properties and related distributions. The book also presents various approaches to estimate the parameters of the Weibull distribution under all possible situations of sampling data as well as approaches to parameter and goodness-of-fit testing. Describes the Statistical Methods Concepts Theories and Applications of This DistributionCompiling findings from dozens of scientific journals and hundreds of research papers the author first gives a careful and thorough mathematical description of the Weibull distribution and all of its features. He then deals with Weibull analysis using classical and Bayesian approaches along with graphical and linear maximum likelihood techniques to estimate the three Weibull parameters. The author also explores the inference of Weibull processes Weibull parameter testing and different types of goodness-of-fit tests and methods. Successfully Apply the Weibull ModelBy using inferential procedures for estimating testing forecasting and simulating data this self-contained detailed handbook shows how to solve statistical life science and engineering problems. | The Weibull Distribution A Handbook

GBP 44.99
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Robust Statistical Methods with R Second Edition

Robust Statistical Methods with R Second Edition

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment and the updated chapter on the multivariate model contains additional material on visualization of multivariate data in R. A new chapter on robust procedures in measurement error models concentrates mainly on the rank procedures less sensitive to errors than other procedures. This book will be an invaluable resource for researchers and postgraduate students in statistics and mathematics. Features• Provides a systematic practical treatment of robust statistical methods• Offers a rigorous treatment of the whole range of robust methods including the sequential versions of estimators their moment convergence and compares their asymptotic and finite-sample behavior• The extended account of multivariate models includes the admissibility shrinkage effects and unbiasedness of two-sample tests• Illustrates the small sensitivity of the rank procedures in the measurement error model• Emphasizes the computational aspects supplies many examples and illustrations and provides the own procedures of the authors in the R software on the book’s website | Robust Statistical Methods with R Second Edition

GBP 39.99
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Introductory Statistical Inference

Introductory Statistical Inference

This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial using worked examples exercises figures tables and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques. Beginning with a review of the basic concepts and methods in probability theory moments and moment generating functions the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables exponential families of distributions and standard probability inequalities. It develops the Helmert transformation for normal distributions introduces the notions of convergence and spotlights the central limit theorems. Coverage highlights sampling distributions Basu's theorem Rao-Blackwellization and the Cramér-Rao inequality. The text also provides in-depth coverage of Lehmann-Scheffé theorems focuses on tests of hypotheses describes Bayesian methods and the Bayes' estimator and develops large-sample inference. The author provides a historical context for statistics and statistical discoveries and answers to a majority of the end-of-chapter exercises. Designed primarily for a one-semester first-year graduate course in probability and statistical inference this text serves readers from varied backgrounds ranging from engineering economics agriculture and bioscience to finance financial mathematics operations and information management and psychology.

GBP 59.99
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The Theory of Statistical Implicative Analysis Or the Implausibility of Falsehood ... When the Exception Confirms the Rule

The Theory of Statistical Implicative Analysis Or the Implausibility of Falsehood ... When the Exception Confirms the Rule

This book summarizes the methods and concepts of Statistical Implicative Analysis (SIA) created by Régis Gras in the 1980s to study in a new way the behavioural responses of French pupils to mathematics tests. Using a multidimensional non-symmetrical data analysis method SIA crosses a set of subjects or objects with a set of variables. It effectively complements traditional correlational and psychometric methods. SIA through its various extensions is today presented as a broad Artificial Intelligence method aimed at extracting trends and possible causalities in the form of rules from a set of variables. It is based on the unlikeliness of the existence of these relationships i. e. on the relative weakness of their counter-examples compared to what chance alone would produce. It establishes a dual topological relationship between the set of subjects and the set of variables. Many applications of this approach driving forces or crucibles for the development of SIA have concerned and still concern various fields such as didactics evaluation and assessment psychology sociology medicine biology economics art history and others. Key Features: Presents the foundations and representations of SIA Provides extensions of variable sets and subjects Includes a bonus exercise | The Theory of Statistical Implicative Analysis Or the Implausibility of Falsehood . When the Exception Confirms the Rule

GBP 120.00
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A Course in the Large Sample Theory of Statistical Inference

A Course in the Large Sample Theory of Statistical Inference

This book provides an accessible but rigorous introduction to asymptotic theory in parametric statistical models. Asymptotic results for estimation and testing are derived using the “moving alternative” formulation due to R. A. Fisher and L. Le Cam. Later chapters include discussions of linear rank statistics and of chi-squared tests for contingency table analysis including situations where parameters are estimated from the complete ungrouped data. This book is based on lecture notes prepared by the first author subsequently edited expanded and updated by the second author. Key features: Succinct account of the concept of “asymptotic linearity” and its uses Simplified derivations of the major results under an assumption of joint asymptotic normality Inclusion of numerical illustrations practical examples and advice Highlighting some unexpected consequences of the theory Large number of exercises many with hints to solutions Some facility with linear algebra and with real analysis including ‘epsilon-delta’ arguments is required. Concepts and results from measure theory are explained when used. Familiarity with undergraduate probability and statistics including basic concepts of estimation and hypothesis testing is necessary and experience with applying these concepts to data analysis would be very helpful. | A Course in the Large Sample Theory of Statistical Inference

GBP 84.99
1

Designing Network On-Chip Architectures in the Nanoscale Era

Designing Network On-Chip Architectures in the Nanoscale Era

Going beyond isolated research ideas and design experiences Designing Network On-Chip Architectures in the Nanoscale Era covers the foundations and design methods of network on-chip (NoC) technology. The contributors draw on their own lessons learned to provide strong practical guidance on various design issues. Exploring the design process of the network the first part of the book focuses on basic aspects of switch architecture and design topology selection and routing implementation. In the second part contributors discuss their experiences in the industry offering a roadmap to recent products. They describe Tilera’s TILE family of multicore processors novel Intel products and research prototypes and the TRIPS operand network (OPN). The last part reveals state-of-the-art solutions to hardware-related issues and explains how to efficiently implement the programming model at the network interface. In the appendix the microarchitectural details of two switch architectures targeting multiprocessor system-on-chips (MPSoCs) and chip multiprocessors (CMPs) can be used as an experimental platform for running tests. A stepping stone to the evolution of future chip architectures this volume provides a how-to guide for designers of current NoCs as well as designers involved with 2015 computing platforms. It cohesively brings together fundamental design issues alternative design paradigms and techniques and the main design tradeoffs—consistently focusing on topics most pertinent to real-world NoC designers.

GBP 59.99
1

Probability Statistics and Data A Fresh Approach Using R

Probability Statistics and Data A Fresh Approach Using R

This book is a fresh approach to a calculus based first course in probability and statistics using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included using calculus when appropriate but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized quantified using simulations and demonstrated with real data. The book compares parametric and non-parametric methods through simulation allowing for a thorough investigation of testing error and power. The text builds R skills from the outset allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers so that you are working with current real data which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr ggplot2 tidyr stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University and the majority of the more than 400 exercises have been classroom tested. | Probability Statistics and Data A Fresh Approach Using R

GBP 82.99
1

Randomization Bootstrap and Monte Carlo Methods in Biology

Randomization Bootstrap and Monte Carlo Methods in Biology

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors the fourth edition of Randomization Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization bootstrapping and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications with data sets available online. Features Presents an overview of computer-intensive statistical methods and applications in biology Covers a wide range of methods including bootstrap Monte Carlo ANOVA regression and Bayesian methods Makes it easy for biologists researchers and students to understand the methods used Provides information about computer programs and packages to implement calculations particularly using R code Includes a large number of real examples from a range of biological disciplines Written in an accessible style with minimal coverage of theoretical details this book provides an excellent introduction to computer-intensive statistical methods for biological researchers. It can be used as a course text for graduate students as well as a reference for researchers from a range of disciplines. The detailed worked examples of real applications will enable practitioners to apply the methods to their own biological data.

GBP 44.99
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Survival Analysis

Survival Analysis

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring truncation and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties essentially asymptotic ones of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model Aalen’s additive hazards model etc. Information criteria to facilitate model selection including Akaike Bayes and Focused Penalized methods Survival trees and ensemble techniques of bagging boosting and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book

GBP 99.99
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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry academia and government. It introduces the tidyverse suite of R packages including the ggplot2 package for data visualization and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses the book covers traditional introductory statistics topics like confidence intervals hypothesis testing and multiple regression modeling while focusing on visualization throughout. Features: ● Assumes minimal prerequisites notably no prior calculus nor coding experience ● Motivates theory using real-world data including all domestic flights leaving New York City in 2013 the Gapminder project and the data journalism website FiveThirtyEight. com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for tidy and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive. com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics at both the undergraduate and graduate levels.

GBP 66.99
1

Principles of Biostatistics

Principles of Biostatistics

Principles of Biostatistics Third Edition is a concepts-based introduction to statistical procedures that prepares public health medical and life sciences students to conduct and evaluate research. With an engaging writing style and helpful graphics the emphasis is on concepts over formulas or rote memorization. Throughout the book the authors use practical interesting examples with real data to bring the material to life. Thoroughly revised and updated this third edition includes a new chapter introducing the basic principles of Study Design as well as new sections on sample size calculations for two-sample tests on means and proportions the Kruskal-Wallis test and the Cox proportional hazards model. Key Features: Includes a new chapter on the basic principles of study design. Additional review exercises have been added to each chapter. Datasets and Stata and R code are available on the book’s website. The book is divided into three parts. The first five chapters deal with collections of numbers and ways in which to summarize explore and explain them. The next two chapters focus on probability and introduce the tools needed for the subsequent investigation of uncertainty. It is only in the eighth chapter and thereafter that the authors distinguish between populations and samples and begin to investigate the inherent variability introduced by sampling thus progressing to inference. Postponing the slightly more difficult concepts until a solid foundation has been established makes it easier for the reader to comprehend them.

GBP 74.99
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Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Reviewing the theory of the general linear model (GLM) using a general framework Univariate and Multivariate General Linear Models: Theory and Applications with SAS Second Edition presents analyses of simple and complex models both univariate and multivariate that employ data sets from a variety of disciplines such as the social and behavioral sciences. With revised examples that include options available using SAS 9. 0 this expanded edition divides theory from applications within each chapter. Following an overview of the GLM the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis and multivariate GLMs that cover multivariate regression analysis MANOVA MANCOVA and repeated measurement data analyses. The book also analyzes double multivariate linear growth curve seeming unrelated regression (SUR) restricted GMANOVA and hierarchical linear models. New to the Second EditionTwo chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedureExpanded theory of unrestricted general linear multivariate general linear SUR and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithmApplications of MI MIANALYZE TRANSREG and CALIS proceduresA practical introduction to GLMs Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework. | Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

GBP 56.99
1

Logistic Regression Models

Logistic Regression Models

Logistic Regression Models presents an overview of the full range of logistic models including binary proportional ordered partially ordered and unordered categorical response regression procedures. Other topics discussed include panel survey skewed penalized and exact logistic models. The text illustrates how to apply the various models to health environmental physical and social science data. Examples illustrate successful modelingThe text first provides basic terminology and concepts before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression varieties of overdispersion and a number of extensions to the basic binary and binomial logistic model. Both real and simulated data are used to explain and test the concepts involved. The appendices give an overview of marginal effects and discrete change as well as a 30-page tutorial on using Stata commands related to the examples used in the text. Stata is used for most examples while R is provided at the end of the chapters to replicate examples in the text. Apply the models to your own dataData files for examples and questions used in the text as well as code for user-authored commands are provided on the book’s website formatted in Stata R Excel SAS SPSS and Limdep. See Professor Hilbe discuss the book.

GBP 52.99
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