Practical statistical methods : a SAS programming approach
- Padgett, Lakshmi V.
- Boca Raton, FL : CRC Press, c2011.
- Physical description
- xiii, 290 p. : ill. ; 25 cm.
QA276.45 .S27 P33 2011
- Unknown QA276.45 .S27 P33 2011
- Includes bibliographical references (p. 279-286) and index.
- Introduction Types of Data Descriptive Statistics/Data Summaries Graphical and Tabular Representation Population and Sample Estimation and Testing Hypothesis Normal Distribution Nonparametric Methods Some Useful Concepts Qualitative Data One Sample Two Independent Samples Paired Two Samples k Independent Samples Cochran's Test Ordinal Data Continuous Normal Data One Sample Two Samples k Independent Samples Multivariate Methods Multifactor ANOVA Variance Components Split Plot Designs Latin Square Design Two Treatment Crossover Design Nonparametric Methods One Sample Two Samples k Samples Transformations Friedman Test Association Measures Censored Data Regression Simple Regression Polynomial Regression Multiple Regressions Diagnostics Weighted Regression Logistic Regression Poisson Regression Robust Regression Nonlinear Regression Piecewise Regression Accelerated Failure Time (AFT) Model Cox Regression Parallelism of Regression Equations Variance-Stabilizing Transformations Ridge Regression Local Regression (LOESS) Response Surface Methodology: Quadratic Model Mixture Designs and Their Analysis Analysis of Longitudinal Data: Mixed Models Miscellaneous Topics Missing Data Diagnostic Errors and Human Behavior Density Estimation Robust Estimators Jackknife Estimators Bootstrap Method Propensity Scores Interim Analysis and Stopping Rules Microarrays and Multiple Testing Stability of Products Group Testing Correspondence Analysis Classification Regression Trees (CARTs) Multidimensional Scaling Path Analysis Choice-Based Conjoint Analysis Meta-Analysis References and Selected Bibliography Index.
- (source: Nielsen Book Data)
- Publisher's 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.
(source: Nielsen Book Data)
- Publication date
- Lakshmi V. Padgett.
- "A Chapman & Hall book."