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Statistical Hypothesis Testing
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.
Hypothesis Testing
If you have a degree in statistics, you probably know how to choose the correct statistical hypothesis test and you might not learn anything from this book. Then again, you just might… Kristen Kehrer, who has a Master’s degree in statistics, said: “Lee Baker has developed a wonderful visual aid which, frankly, I wish I had when I was first learning about all the different types of test statistics”. The aid she’s talking about is a statistical test flow chart that I call The Hypothesis Wheel, and is what you’ll learn about in Hypothesis Testing. If you’re one of the 99% of researchers and analysts who use statistics but have never studied it at University, then this book is for ...
Testing Statistical Hypotheses
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.
Statistical Hypothesis Testing with Microsoft ® Office Excel ®
This book provides a comprehensive treatment of the logic behind hypothesis testing. Readers will learn to understand statistical hypothesis testing and how to interpret P-values under a variety of conditions including a single hypothesis test, a collection of hypothesis tests, and tests performed on accumulating data. The author explains how a hypothesis test can be interpreted to draw conclusions, and descriptions of the logic behind frequentist (classical) and Bayesian approaches to interpret the results of a statistical hypothesis test are provided. Both approaches have their own strengths and challenges, and a special challenge presents itself when hypothesis tests are repeatedly performed on accumulating data. Possible pitfalls and methods to interpret hypothesis tests when accumulating data are also analyzed. This book will be of interest to researchers, graduate students, and anyone who has to interpret the results of statistical analyses.
Theory of Point Estimation
This second, much enlarged edition by Lehmann and Casella of Lehmann's classic text on point estimation maintains the outlook and general style of the first edition. All of the topics are updated, while an entirely new chapter on Bayesian and hierarchical Bayesian approaches is provided, and there is much new material on simultaneous estimation. Each chapter concludes with a Notes section which contains suggestions for further study. This is a companion volume to the second edition of Lehmann's "Testing Statistical Hypotheses".
Statistical Power Analysis
CD-ROM contains: "easy-to-use statistical power analysis program."
Learning Statistics with R
"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Testing Statistical Hypotheses
Testing Statistical Hypotheses, 4th Edition updates and expands upon the classic graduate text, now a two-volume set. The first volume covers finite-sample theory, while the second volume discusses large-sample theory. A definitive resource for graduate students and researchers alike, this work grows to include new topics of current relevance. New additions include an expanded treatment of multiple hypothesis testing, a new section on extensions of the Central Limit Theorem, coverage of high-dimensional testing, expanded discussions of permutation and randomization tests, coverage of testing moment inequalities, and many new problems throughout the text.
Statistics for the Behavioral Sciences
Statistics for the Behavioral Sciences is an introduction to statistics text that will engage students in an ongoing spirit of discovery by illustrating how statistics apply to modern-day research problems. By integrating instructions, screenshots, and practical examples for using IBM SPSS® Statistics software, the book makes it easy for students to learn statistical concepts within each chapter. Gregory J. Privitera takes a user-friendly approach while balancing statistical theory, computation, and application with the technical instruction needed for students to succeed in the modern era of data collection, analysis, and statistical interpretation.
Statistical Power Analysis
Noted for its accessible approach, this bestseller applies power analysis to both null hypothesis and minimum-effect testing using the same basic model. Through the use of a few relatively simple procedures and examples from the behavioral and social sciences, the authors show readers with little expertise in statistical analysis how to quickly obtain the values needed to carry out the power analysis for their research. Illustrations of how these analyses work and how they can be used to understand problems of study design, to evaluate research, and to choose the appropriate criterion for defining "statistically significant" outcomes are sprinkled throughout. The book presents a simple and g...
