Practical Statistical Methods A SAS Programming Approach

About this book

This reference presents a broad range of statistical methods for a diverse audience. It first introduces fundamental concepts from statistics and then discusses data analysis and regression. Later chapters cover such diverse topics as interim analysis, propensity scores, and competing risks. The author provides SAS code for the analyses of widely encountered research problems found in a range of disciplines, including medical research, the pharmaceutical industry, and the social sciences. While SAS code and output is integrated into the text, a supporting website includes all of the data sets and SAS code.

Contents

Introduction Types of Data Descriptive Statistics/Data Summaries Graphical and Tabular Representation Population and Sample Estimation and Testing Hypothesis Normal Distribution Non-Parametric 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 Samples Multivariate Methods Multi-Way ANOVA Cross-Over Designs Non-Parametric Methods One Sample Two Samples Kruskal--Wallis Test Friedman Test Censored Data Density Estimation Regression Simple Regression Correlation and Partial Correlation Polynomial Regression Multiple Regression Diagnostics Weighted Regression Logistic Regression Poisson Regression Over Dispersion Cox Regression Miscellaneous Topics Conditional Power Interim Analysis Misclassification Errors Cut-off Points on Markers Indicating End Point Propensity Scores Competing Risk

Customer Reviews

Biography

Lakshmi Padgett is lead statistician at Centocor in Malvern, Pennsylvania.