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Author Padgett, Lakshmi V

Title Practical statistical methods : a SAS programming approach / Lakshmi V. Padgett
Published Boca Raton, FL : CRC Press, ©2011

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Description 1 online resource (xiii, 290 pages) : illustrations
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
Summary Practical Statistical Methods: A SAS Programming Approach presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems. After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife. With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences
Bibliography Includes bibliographical references and index
Notes Print version record
Subject SAS (Computer program language)
Mathematical statistics -- Data processing.
Probabilities -- Data processing
MATHEMATICS -- Probability & Statistics -- General.
Mathematical statistics -- Data processing
Probabilities -- Data processing
SAS (Computer program language)
Form Electronic book
ISBN 9781439812549
1439812543