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Practical statistical methods : a SAS programming approach / Lakshmi V. Padgett.

By: Material type: TextTextPublication details: Boca Raton, FL : CRC Press, c2011.Description: xiii, 290 p. : ill. ; 25 cmISBN:
  • 9781439812822 (hardcover : alk. paper)
  • 1439812829 (hardcover : alk. paper)
  • 9780415804318
  • 0415804310
Subject(s): DDC classification:
  • 519.50285 23
LOC classification:
  • QA276.45.S27 P33 2011
Online resources:
Contents:
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.
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Includes bibliographical references and index.

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.

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