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.

Klappentext

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.

Inhalt

Contents: A Thoughtful Student's Retrospective on Statistics 101.- Testing for Normality.- Testing for Homoscedasticity.- Testing for Independence of Observations.- Identification of Outliers.- Transformations.- Independent Variable Selection in Multiple Regression.- Categorical Variables in Regression.- Analysis of Cross-Classified Data.- Index of Reference Tables.- Index.