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This book presents up-to-date theory and methods of statistical hypothesis testing based on measure theory. The so-called statistical space is a measurable space adding a family of probability measures. Most topics in the book will be developed based on this term. The book includes some typical data sets, such as the relation between race and the death penalty verdict, the behavior of food intake of two kinds of Zucker rats, and the per capita income and expenditure in China during the 1978–2002 period. Emphasis is given to the process of finding appropriate statistical techniques and methods of evaluating these techniques.

As a self-study guide or for classroom use, this text is designed for students who have math anxiety. The book begins with a nonmathematical introduction to the major statistical concepts. Once mathematical symbols are introduced, they are carefully explained in words. Numerous fully worked problems with step-by-step solutions are included. Integrated multiple-choice questions make this text ideal for use with clickers in the assessment phase of a flipped classroom environment. Cooperative learning exercises are presented to foster deep levels of cognition, perfect for the latter phase of the flipped classroom. Group projects with peer assessment forms are also included to allow students to experience how statistical inference is used in practice.

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Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models, with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i.i.d. and general non-i.i.d. stochastic models in detail, including Markov, hidden Markov, state-space, regression, and autoregression models. Rigorous proofs are given for the most important results. Written by leading authorities in the field, this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms.

Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models, with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i.i.d. and general non-i.i.d. stochastic models in detail, including Markov, hidden Markov, state-space, regression, and autoregression models. Rigorous proofs are given for the most important results. Written by leading authorities in the field, this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms.

Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, is a ‘how-to’ on the application of robust methods using available software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. Since the last edition, there have been numerous advances and improvements. They include new techniques for comparing groups and measuring effect size as well as new methods for comparing quantiles. Many new regression methods have been added that include both parametric and nonparametric techniques. The methods related to ANCOVA have been expanded considerably. New perspectives related to discrete distributions with a relatively small sample space are described as well as new results relevant to the shift function. The practical importance of these methods is illustrated using data from real world studies. The R package written for this book now contains over 1200 functions. New to this edition 35% revised contentCovers many new and improved R functionsNew techniques that deal with a wide range of situationsExtensive revisions to cover the latest developments in robust regressionCovers latest improvements in ANOVAIncludes newest rank-based methodsDescribes and illustrated easy to use software

Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, is a ‘how-to’ on the application of robust methods using available software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. Since the last edition, there have been numerous advances and improvements. They include new techniques for comparing groups and measuring effect size as well as new methods for comparing quantiles. Many new regression methods have been added that include both parametric and nonparametric techniques. The methods related to ANCOVA have been expanded considerably. New perspectives related to discrete distributions with a relatively small sample space are described as well as new results relevant to the shift function. The practical importance of these methods is illustrated using data from real world studies. The R package written for this book now contains over 1200 functions. New to this edition 35% revised contentCovers many new and improved R functionsNew techniques that deal with a wide range of situationsExtensive revisions to cover the latest developments in robust regressionCovers latest improvements in ANOVAIncludes newest rank-based methodsDescribes and illustrated easy to use software

The Phylogenetic Handbook is a broad, hands on guide to theory and practice of nucleotide and protein phylogenetic analysis. This second edition includes six new chapters, covering topics such as Bayesian inference, tree topology testing and the impact of recombination on phylogenies, as well as a detailed section on molecular adaptation. The book has a stronger focus on hypothesis testing than the previous edition, with more extensive discussions on recombination analysis, detecting molecular adaptation and genealogy-based population genetics. Many chapters include elaborate practical sections, which have been updated to introduce the reader to the most recent versions of sequence analysis and phylogeny software, including BLAST, FastA, Clustal, T-coffee, Muscle, DAMBE, Tree-puzzle, Phylip, MEGA, PAUP*, IQPNNI, CONSEL, ModelTest, Prottest, PAML, HYPHY, MrBayes, BEAST, LAMARC, SplitsTree, and RDP. Many analysis tools are described by their original authors, resulting in clear explanations that constitute an ideal teaching guide for advanced-level undergraduate and graduate students.

Quantitative and Statistical Research Methods This user-friendly textbook teaches students to understand and apply procedural steps in completing quantitative studies. It explains statistics while progressing through the steps of the hypothesis-testing process from hypothesis to results. The research problems used in the book reflect statistical applications related to interesting and important topics. In addition, the book provides a Research Analysis and Interpretation Guide to help students analyze research articles. Designed as a hands-on resource, each chapter covers a single research problem and offers directions for implementing the research method from start to finish. Readers will learn how to: Pinpoint research questions and hypotheses Identify, classify, and operationally define the study variables Choose appropriate research designs Conduct power analysis Select an appropriate statistic for the problem Use a data set Conduct data screening and analyses using SPSS Interpret the statistics Write the results related to the problem Quantitative and Statistical Research Methods allows students to immediately, independently, and successfully apply quantitative methods to their own research projects.

This book provides a concise and integrated overview of hypothesis testing in four important subject areas, namely linear and nonlinear models, multivariate analysis, and large sample theory. The approach used is a geometrical one based on the concept of projections and their associated idempotent matrices, thus largely avoiding the need to involvematrix ranks. It is shown that all the hypotheses encountered are either linear or asymptotically linear, and that all the underlying models used are either exactly or asymptotically linear normal models. This equivalence can be used, for example, to extend the concept of orthogonality to other models in the analysis of variance, and to show that the asymptotic equivalence of the likelihood ratio, Wald, and Score (Lagrange Multiplier) hypothesis tests generally applies.

Quantitative and Statistical Research Methods This user-friendly textbook teaches students to understand and apply procedural steps in completing quantitative studies. It explains statistics while progressing through the steps of the hypothesis-testing process from hypothesis to results. The research problems used in the book reflect statistical applications related to interesting and important topics. In addition, the book provides a Research Analysis and Interpretation Guide to help students analyze research articles. Designed as a hands-on resource, each chapter covers a single research problem and offers directions for implementing the research method from start to finish. Readers will learn how to: Pinpoint research questions and hypotheses Identify, classify, and operationally define the study variables Choose appropriate research designs Conduct power analysis Select an appropriate statistic for the problem Use a data set Conduct data screening and analyses using SPSS Interpret the statistics Write the results related to the problem Quantitative and Statistical Research Methods allows students to immediately, independently, and successfully apply quantitative methods to their own research projects.

How to use computer-intensive methods to assess the significance of a statistic in an hypothesis test--for both statisticians and nonstatisticians alike. The significance of almost any test can be assessed using one of the methods presented here, for the techniques given are very general (e.g. virtually every nonparametric statistical test is a special case of one of the methods covered). Programs presented are brief, easy to read, require minimal programming, and can be run on most PC's. They also serve as templates adaptable to a wide range of applications. Includes numerous illustrations of how to apply computer-intensive methods.

Voice Rehabilitation: Testing Hypotheses and Reframing Therapy offersa patient-centered, hypothesis-driven framework for clinicians beginningto practice voice rehabilitation as well as practicing clinicians who continueto develop their skills. This valuable resource integrates motor learningtheory with the physiological underpinnings of voice production to make therehabilitation process more accessible and cohesive. Dialogues betweenthe patient and the clinician interwoven with the voice clinician’s internalmonologue provide insight into the active clinical reasoning process. A review of the etiologies and physiological changes associated with frequently diagnosed laryngeal pathologies provides a useful reference.FEATURES· Video clips featuring in-session demonstration and modification of procedures· Step-by-step description of voice exercise and rationales for their implementation· Special chapter devoted to the singer, transgender client, and patient who suffers from spasmodic dysphonia· Framework for group voice therapy· Counseling to address adherence, readiness for change, and generalization· Comprehensive discussion of risk factors for professional and customary voice users· Chapter Objectives, Review of Laryngeal Pathologies, Voice Evaluation ProtocolTEACHING TOOLS· Instructor’s Manual, featuring frameworks for class discussions; Case Studies; and Key Chapter Terms and ConceptsSTUDENT RESOURCESNavigate Companion Website*, including:· Interactive Learning Activities· Interactive Glossary· Videos*Each new copy of the text includes an access code to the Navigate Companion Website

The third edition of Testing Statistical Hypotheses updates and expands upon the classic graduate text, emphasizing optimality theory for hypothesis testing and confidence sets. The principal additions include a rigorous treatment of large sample optimality, together with the requisite tools. In addition, an introduction to the theory of resampling methods such as the bootstrap is developed. The sections on multiple testing and goodness of fit testing are expanded. The text is suitable for Ph.D. students in statistics and includes over 300 new problems out of a total of more than 760.

How to use computer-intensive methods to assess the significance of a statistic in an hypothesis test--for both statisticians and nonstatisticians alike. The significance of almost any test can be assessed using one of the methods presented here, for the techniques given are very general (e.g. virtually every nonparametric statistical test is a special case of one of the methods covered). Programs presented are brief, easy to read, require minimal programming, and can be run on most PC's. They also serve as templates adaptable to a wide range of applications. Includes numerous illustrations of how to apply computer-intensive methods.

The third edition of Testing Statistical Hypotheses updates and expands upon the classic graduate text, emphasizing optimality theory for hypothesis testing and confidence sets. The principal additions include a rigorous treatment of large sample optimality, together with the requisite tools. In addition, an introduction to the theory of resampling methods such as the bootstrap is developed. The sections on multiple testing and goodness of fit testing are expanded. The text is suitable for Ph.D. students in statistics and includes over 300 new problems out of a total of more than 760.

Significance testing - a core technique in statistics for hypothesis testing - is introduced in this volume. Mohr first reviews what is meant by sampling and probability distributions and then examines in-depth normal and t-tests of significance. The uses and misuses of significance testing are also explored.

Voice Rehabilitation: Testing Hypotheses and Reframing Therapy offersa patient-centered, hypothesis-driven framework for clinicians beginningto practice voice rehabilitation as well as practicing clinicians who continueto develop their skills. This valuable resource integrates motor learningtheory with the physiological underpinnings of voice production to make therehabilitation process more accessible and cohesive. Dialogues betweenthe patient and the clinician interwoven with the voice clinician’s internalmonologue provide insight into the active clinical reasoning process. A review of the etiologies and physiological changes associated with frequently diagnosed laryngeal pathologies provides a useful reference.FEATURES· Video clips featuring in-session demonstration and modification of procedures· Step-by-step description of voice exercise and rationales for their implementation· Special chapter devoted to the singer, transgender client, and patient who suffers from spasmodic dysphonia· Framework for group voice therapy· Counseling to address adherence, readiness for change, and generalization· Comprehensive discussion of risk factors for professional and customary voice users· Chapter Objectives, Review of Laryngeal Pathologies, Voice Evaluation ProtocolTEACHING TOOLS· Instructor’s Manual, featuring frameworks for class discussions; Case Studies; and Key Chapter Terms and ConceptsSTUDENT RESOURCESNavigate Companion Website*, including:· Interactive Learning Activities· Interactive Glossary· Videos*Each new copy of the text includes an access code to the Navigate Companion Website

This book provides a rigorous introduction to the basic aspects of the theory of linear estimation and hypothesis testing, covering the necessary prerequisites in matrices, multivariate normal distribution and distributions of quadratic forms along the way. It will appeal to advanced undergraduate and first-year graduate students, research mathematicians and statisticians.