Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, this text helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method.
Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, this text helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method.
Peter H. Westfall is the Paul Whitfield Horn Professor of Statistics and James Niver Professor of Information Systems and Quantitative Sciences at Texas Tech University. A Fellow of the ASA and the AAAS, Dr. Westfall has published several books and over 100 papers on statistical theory and methods. He also has won several teaching awards and is the former editor of The American Statistician. He earned a PhD in statistics from the University of California, Davis. Kevin S.S. Henning is a clinical assistant professor of business analysis in the Department of Economics and International Business at Sam Houston State University, where he teaches business statistics and forecasting. He earned a PhD in business statistics from Texas Tech University.
Inhaltsangabe
Introduction: Probability, Statistics and Science. Random Variables and Their Probability Distributions. Probability Calculation and Simulation. Identifying Distributions. Conditional Distributions and Independence. Marginal Distributions, Joint Distributions, Independence, and Bayes' Theorem. Sampling from Populations and Processes. Expected Value and the Law of Large Numbers. Functions of Random Variables: Their Distributions and Expected Values. Distributions of Totals. Estimation: Unbiasedness, Consistency, and Efficiency. The Likelihood Function and Maximum Likelihood Estimates. Bayesian Statistics. Frequentist Statistical Methods. Are Your Results Explainable by Chance Alone? Chi-Squared, Student's t, and F -Distributions, with Applications. Likelihood Ratio Tests. Sample Size and Power. Robustness and Nonparametric Methods. Final Words. Index.
Introduction: Probability, Statistics and Science. Random Variables and Their Probability Distributions. Probability Calculation and Simulation. Identifying Distributions. Conditional Distributions and Independence. Marginal Distributions, Joint Distributions, Independence, and Bayes' Theorem. Sampling from Populations and Processes. Expected Value and the Law of Large Numbers. Functions of Random Variables: Their Distributions and Expected Values. Distributions of Totals. Estimation: Unbiasedness, Consistency, and Efficiency. The Likelihood Function and Maximum Likelihood Estimates. Bayesian Statistics. Frequentist Statistical Methods. Are Your Results Explainable by Chance Alone? Chi-Squared, Student's t, and F -Distributions, with Applications. Likelihood Ratio Tests. Sample Size and Power. Robustness and Nonparametric Methods. Final Words. Index.
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