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.

Inhalt
0 A Thoughtful Student's Retrospective on Statistics 101.- 0. Introduction.- 1. The Introductory Model.- 2. The Regression Model.- References.- 1 Testing for Normality.- 0. Introduction.- 1. Normal Plots.- 2. Regression Procedures.- 3. Studentized Range.- 4. Moment Checking.- 5. Standard Tests of Goodness-of-Fit.- 6. Evaluation.- Appendix I.- References.- 2 Testing for Homoscedasticity.- 0. Introduction.- 1. Comparing Variances of Two Normal Distributions.- 2. Testing Homoscedasticity of Many Populations.- 3. Regression Residuals.- 4. Testing Homoscedasticity of Regression Residuals.- Appendix I.- References.- 3 Testing for Independence of Observations.- 0. Introduction.- 1. Parametric Procedures.- 2. Nonparametric Procedures.- References.- 4 Identification of Outliers.- 0. Introduction.- 1. Normal Distribution.- 2. Nonparametric Procedures.- 3. Outliers in Regression.- References.- 5 Transformations.- 0. Introduction.- 1. Deflating Heteroscedastic Regressions.- 2. Variance Stabilizing Transformations.- 3. Power Transformations (BoxCox).- 4. Letter-Values and Boxplots.- 5. Power Transformations of Regression Independent Variables.- Appendix I.- References.- 6 Independent Variable Selection in Multiple Regression.- 0. Introduction.- 1. Criteria for Goodness of Regression Model.- 2. Stepwise Procedures.- 3. Multicollinearity.- References.- 7 Categorical Variables in Regression.- 0. Introduction.- 1. Two Sample Tests.- 2. Analysis of Variance via Regression (Model I).- 3. Components of Variance (Model II).- 4. Dichotomous Dependent Variables.- References.- 8 Analysis of Cross-Classified Data.- 0. Introduction.- 1. Independence in the r1 × r2 Table.- 2. Log-Linear Models in the r1 × r2 Table.- 3. The Three-Dimensional Table.- 4. Analysis of Cross-Classifications withOrdered Categories.- 5. Latent Class Model.- Appendix I.- Appendix II.- References.- Index of Reference Tables.