Practical statistical methods : a SAS programming approach

Content: 1. Introduction --
1.1. Types of Data --
1.2. Descriptive Statistics/Data Summaries --
1.3. Graphical and Tabular Representation --
1.4. Population and Sample --
1.5. Estimation and Testing Hypothesis --
1.6. Normal Distribution --
1.7. Nonparametric Methods --
1.8. Some Useful Concepts --
2. Qualitative Data --
2.1. One Sample --
2.1.1. Binary Data --
2.1.2. t Categorical Responses --
2.2. Two Independent Samples --
2.2.1. Two Proportions --
2.2.2. Odds Ratio and Relative Risk --
2.2.3. Logistic Regression with One Dichotomous Explanatory Variable --
2.2.4. Cochran-Mantel-Haenszel Test for a 2 x 2 Table --
2.2.5. t Categorical Responses --
2.3. Paired Two Samples --
2.3.1. Binary Responses --
2.3.2. t Categorical Responses --
2.4. k Independent Samples --
2.4.1. k Proportions --
2.4.2. Logistic Regression When the Explanatory Variable Is Not Dichotomous 2.4.3. CMH Test --
2.4.4. t Categorical Responses --
2.5. Cochran's Test --
2.6. Ordinal Data --
2.6.1. Row Mean Score Test --
2.6.2. Cochran-Armitage Test --
2.6.3. Measures of Association --
2.6.4. Ridit Analysis --
2.6.5. Weighted Kappa --
2.6.6. Ordinal Logistic Regression --
2.6.6.1. Two Samples --
2.6.6.2. k Samples --
3. Continuous Normal Data --
3.1. One Sample --
3.2. Two Samples --
3.2.1. Independent Samples --
3.2.1.1. Means --
3.2.1.2. Variances --
3.2.2. Paired Samples --
3.3. k Independent Samples --
3.3.1. One-Way Analysis of Variance --
3.3.1.1. Variance --
3.3.2. Covariance Analysis --
3.4. Multivariate Methods --
3.4.1. Correlation, Partial, and Intraclass Correlation --
3.4.2. Hotelling's T2 --
3.4.2.1. One Sample --
3.4.2.2. Two Samples --
3.4.3. One-Way Multivariate Analysis of Variance --
3.4.4. Profile Analysis --
3.4.5. Discriminant Functions --
3.4.6. Cluster Analysis --
3.4.7. Principal Components 3.4.8. Factor Analysis --
3.4.9. Canonical Correlation --
3.5. Multifactor ANOVA --
3.5.1. Crossed Factors --
3.5.2. Tukey 1 df for Nonadditivity --
3.5.3. Nested Factors --
3.6. Variance Components --
3.7. Split Plot Designs --
3.8. Latin Square Design --
3.9. Two-Treatment Crossover Design --
4. Nonparametric Methods --
4.1. One Sample --
4.1.1. Sign Test --
4.1.2. Wilcoxon Signed-Rank Test --
4.1.3. Kolmogorov Goodness of Fit --
4.1.4. Cox and Stuart Test --
4.2. Two Samples --
4.2.1. Wilcoxon-Mann-Whitney Test --
4.2.2. Mood's Median Test --
4.2.3. Kolmogorov-Smirnov --
4.2.4. Equality of Variances --
4.3. k Samples --
4.3.1. Kruskal-Wallis Test --
4.3.2. Median Test --
4.3.3. Jonckheere Test --
4.4. Transformations --
4.5. Friedman Test --
4.6. Association Measures --
4.6.1. Spearman Rank Correlation --
4.6.2. Kendall's Tau --
4.6.3. Kappa Statistic --
4.7. Censored Data 4.7.1. Kaplan-Meier Survival Distribution Function --
4.7.2. Wilcoxon (Gehan) and Log-Rank Test --
4.7.3. Life-Table (Acturial Method) --
5. Regression --
5.1. Simple Regression --
5.2. Polynomial Regression --
5.3. Multiple Regressions --
5.3.1. Multicollinearity --
5.3.2. Dummy Variables --
5.3.3. Interaction --
5.3.4. Variable Selection --
5.4. Diagnostics --
5.4.1. Outliers --
5.4.2. Influential Observations --
5.4.3. Durbin-Watson Statistic --
5.5. Weighted Regression --
5.6. Logistic Regression --
5.6.1. Dichotomous Logistic Regression --
5.6.2. Multinomial Logistic Model --
5.6.3. Cumulative Logistic Model --
5.7. Poisson Regression --
5.8. Robust Regression --
5.9. Nonlinear Regression --
5.10. Piecewise Regression --
5.11. Accelerated Failure Time (AFT) Model --
5.12. Cox Regression --
5.12.1. Proportional Hazards Model --
5.12.2. Proportional Hazard Assumption --
5.12.3. Stratified Cox Model 5.12.4. Time-Varying Covariates --
5.12.5. Competing Risks --
5.13. Parallelism of Regression Equations --
5.14. Variance-Stabilizing Transformations --
5.15. Ridge Regression --
5.16. Local Regression (LOESS) --
5.17. Response Surface Methodology: Quadratic Model --
5.18. Mixture Designs and Their Analysis --
5.19. Analysis of Longitudinal Data: Mixed Models --
6. Miscellaneous Topics --
6.1. Missing Data --
6.2. Diagnostic Errors and Human Behavior --
6.2.1. Introduction --
6.2.2. Independent Samples --
6.2.2.1. Two Independent Samples --
6.2.2.2. k Independent Samples --
6.2.3. Two Dependent Samples --
6.2.4. Finding the Threshold for a Screening Variable --
6.2.5. Analyzing Response Data with Errors --
6.2.6. Responders' Anonymity --
6.3. Density Estimation --
6.3.1. Parametric Density Estimation --
6.3.2. Nonparametric Univariate Density Estimation --
6.3.3. Bivariate Kernel Estimator --
6.4. Robust Estimators 6.5. Jackknife Estimators --
6.6. Bootstrap Method --
6.7. Propensity Scores --
6.8. Interim Analysis and Stopping Rules --
6.8.1. Stopping Rules --
6.8.2. Conditional Power --
6.9. Microarrays and Multiple Testing --
6.9.1. Microarrays --
6.9.2. Multiple Testing --
6.10. Stability of Products --
6.11. Group Testing --
6.12. Correspondence Analysis --
6.13. Classification Regression Trees --
6.14. Multidimensional Scaling --
6.15. Path Analysis --
6.16. Choice-Based Conjoint Analysis --
6.16.1. Availability Designs and Cross Effects --
6.16.2. Pareto-Optimal Choice Sets --
6.16.3. Mixture-Amount Designs --
6.17. Meta-Analysis --
6.17.1. Homogeneity of the Effect Sizes --
6.17.2. Combining the p-Values.