Ebook: Prescriptions for Working Statisticians

The first course in statistics, no matter how "good" or "long" it is, typically covers inferential procedures which are valid only if a number of preconditions are satisfied by the data. For example, students are taught about regression procedures valid only if the true residuals are independent, homoscedastic, and normally distributed. But they do not learn how to check for indepen­ dence, homoscedasticity, or normality, and certainly do not learn how to adjust their data and/or model so that these assumptions are met. To help this student out! I designed a second course, containing a collec­ tion of statistical diagnostics and prescriptions necessary for the applied statistician so that he can deal with the realities of inference from data, and not merely with the kind of classroom problems where all the data satisfy the assumptions associated with the technique to be taught. At the same time I realized that I was writing a book for a wider audience, namely all those away from the classroom whose formal statistics education ended with such a course and who apply statistical techniques to data.

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The first course in Statistics typically covers inferential procedures which are valid only if a number of preconditions are satisfied by the data. This book, designed for a second course, contains a collection of statistical diagnostics and prescriptions necessary for the applied statistician so that he can deal with the realities of inference from data and not merely with the kind of classroom problems where all the data satisfy the assumptions associated with the technique being taught. The book begins with four chapters on data diagnostics, and then proceeds to discuss prescriptions for using the data, given its diagnosed characteristics. The book concludes with two chapters on techniques for making inferences from specialized data, mixing categorical and measured data, and cross-classified data.

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The first course in Statistics typically covers inferential procedures which are valid only if a number of preconditions are satisfied by the data. This book, designed for a second course, contains a collection of statistical diagnostics and prescriptions necessary for the applied statistician so that he can deal with the realities of inference from data and not merely with the kind of classroom problems where all the data satisfy the assumptions associated with the technique being taught. The book begins with four chapters on data diagnostics, and then proceeds to discuss prescriptions for using the data, given its diagnosed characteristics. The book concludes with two chapters on techniques for making inferences from specialized data, mixing categorical and measured data, and cross-classified data.
Content:
Front Matter....Pages i-xix
A Thoughtful Student’s Retrospective on Statistics 101....Pages 1-13
Testing for Normality....Pages 14-55
Testing for Homoscedasticity....Pages 56-91
Testing for Independence of Observations....Pages 92-119
Identification of Outliers....Pages 120-147
Transformations....Pages 148-180
Independent Variable Selection in Multiple Regression....Pages 181-213
Categorical Variables in Regression....Pages 214-251
Analysis of Cross-Classified Data....Pages 252-287
Back Matter....Pages 289-295

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The first course in Statistics typically covers inferential procedures which are valid only if a number of preconditions are satisfied by the data. This book, designed for a second course, contains a collection of statistical diagnostics and prescriptions necessary for the applied statistician so that he can deal with the realities of inference from data and not merely with the kind of classroom problems where all the data satisfy the assumptions associated with the technique being taught. The book begins with four chapters on data diagnostics, and then proceeds to discuss prescriptions for using the data, given its diagnosed characteristics. The book concludes with two chapters on techniques for making inferences from specialized data, mixing categorical and measured data, and cross-classified data.
Content:
Front Matter....Pages i-xix
A Thoughtful Student’s Retrospective on Statistics 101....Pages 1-13
Testing for Normality....Pages 14-55
Testing for Homoscedasticity....Pages 56-91
Testing for Independence of Observations....Pages 92-119
Identification of Outliers....Pages 120-147
Transformations....Pages 148-180
Independent Variable Selection in Multiple Regression....Pages 181-213
Categorical Variables in Regression....Pages 214-251
Analysis of Cross-Classified Data....Pages 252-287
Back Matter....Pages 289-295
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