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Understanding advanced statistical methods / Peter H. Westfall and Kevin S. S. Henning.

By: Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC texts in statistical science seriesPublication details: Boca Raton : CRC Press, c2013.Description: xxv, 543 p. : illustrations ; 26 cmISBN:
  • 9781466512108 (hardback)
Subject(s): DDC classification:
  • 23 W527 000SA.01
Contents:
1. Introduction: Probability, statistics, and science-- 2. Random variables and their probability distributions-- 3. Probability calculation and simulation-- 4. Indentifying distributions-- 5. Conditional distributions and independence-- 6. Marginal distributions, joint distributions, independence and Bayes' theorem-- 7. Sampling from populations and processes-- 8. Expected value and the law of large numbers-- 9. Functions of random variables: their distributions and expected values-- 10. Distributions of totals-- 11. Estimation: Unbiasedness, consistency, and efficiency-- 12. Likelihood function and maximum likelihood estimates-- 13. Bayesian statistics-- 14. Frequentist statistical methods-- 15. Are your results explainable by chance alone? 16. Chi-squared, student's and F-distributions, with applications-- 17. Likelihood ratio tests-- 18. Sample size and power-- 19. Robustness and nonparametric methods-- 20. Final words-- Index.
Summary: "Preface We wrote this book because there is a large gap between the elementary statistics course that most people take and the more advanced research methods courses taken by graduate and upper-division students so they can carry out research projects. These advanced courses include difficult topics such as regression, forecasting, structural equations, survival analysis, and categorical data, often analyzed using sophisticated likelihood-based and even Bayesian methods. However, they typically devote little time to helping students understand the fundamental assumptions and machinery behind these methods. Instead, they teach the material like witchcraft: Do this, do that, and voilà--statistics! Students thus have little idea as to what they are doing and why they are doing it. Like trained parrots, they learn how to recite statistical jargon mindlessly. The goal of this book is to make statistics less like witchcraft and to treat students like intelligent humans and not like trained parrots--thus the title, Understanding Advanced Statistical Methods. This book will surprise your students. It will cause them to think differently about things, not only about math and statistics, but also about research, the scientific method, and life in general. It will teach them how to do good modeling--and hence good statistics-- from a standpoint of deep knowledge rather than rote knowledge. It will also provide them with tools to think critically about the claims they see in the popular press and to design their own studies to avoid common errors"--
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Includes bibliographical references and index.

1. Introduction: Probability, statistics, and science--
2. Random variables and their probability distributions--
3. Probability calculation and simulation--
4. Indentifying distributions--
5. Conditional distributions and independence--
6. Marginal distributions, joint distributions, independence and Bayes' theorem--
7. Sampling from populations and processes--
8. Expected value and the law of large numbers--
9. Functions of random variables: their distributions and expected values--
10. Distributions of totals--
11. Estimation: Unbiasedness, consistency, and efficiency--
12. Likelihood function and maximum likelihood estimates--
13. Bayesian statistics--
14. Frequentist statistical methods--
15. Are your results explainable by chance alone?
16. Chi-squared, student's and F-distributions, with applications--
17. Likelihood ratio tests--
18. Sample size and power--
19. Robustness and nonparametric methods--
20. Final words--
Index.

"Preface We wrote this book because there is a large gap between the elementary statistics course that most people take and the more advanced research methods courses taken by graduate and upper-division students so they can carry out research projects. These advanced courses include difficult topics such as regression, forecasting, structural equations, survival analysis, and categorical data, often analyzed using sophisticated likelihood-based and even Bayesian methods. However, they typically devote little time to helping students understand the fundamental assumptions and machinery behind these methods. Instead, they teach the material like witchcraft: Do this, do that, and voilà--statistics! Students thus have little idea as to what they are doing and why they are doing it. Like trained parrots, they learn how to recite statistical jargon mindlessly. The goal of this book is to make statistics less like witchcraft and to treat students like intelligent humans and not like trained parrots--thus the title, Understanding Advanced Statistical Methods. This book will surprise your students. It will cause them to think differently about things, not only about math and statistics, but also about research, the scientific method, and life in general. It will teach them how to do good modeling--and hence good statistics-- from a standpoint of deep knowledge rather than rote knowledge. It will also provide them with tools to think critically about the claims they see in the popular press and to design their own studies to avoid common errors"--

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