Taeger D., Kuhnt S. Statistical Hypothesis Testing with SAS and R

Wiley, 2014. — 308 p. — ISBN: 978-1-119-95021-9.
A comprehensive guide to statistical hypothesis testing with examples in SAS and R.
When analyzing datasets the following questions often arise:
Is there a short hand procedure for a statistical test available in SAS or R?
If so, how do I use it?
If not, how do I program the test myself?
This book answers these questions and provides an overview of the most common statistical test problems in a comprehensive way, making it easy to find and perform an appropriate statistical test.
A general summary of statistical test theory is presented, along with a basic description for each test, including the necessary prerequisites, assumptions, the formal test problem and the test statistic. Examples in both SAS and R are provided, along with program code to perform the test, resulting output and remarks explaining the necessary program parameters.Key featuresProvides examples in both SAS and R for each test presented.
Looks at the most common statistical tests, displayed in a clear and easy to follow way.Academics, practitioners and SAS and R programmers will find this book a valuable resource. Students using SAS and R will also find it an excellent choice for reference and data analysis.Statistical hypothesis testing.
Normal Distribution
Tests on the mean.
Tests on the variance.
Binomial Distribution
Tests on proportions.
Other Distributions
Poisson distribution.
Exponential distribution.
Correlation
Tests on association.
Nonparametric Tests
Tests on location.
Tests on scale difference.
Other tests.
Goodness-of-Fit Tests
Tests on normality.
Tests on other distributions.
Tests on Randomness
Tests on randomness.
Tests on Contingency Tables
Tests on contingency tables.
Tests on Outliers
Tests on outliers.
Tests in Regression Analysis
Tests in regression analysis.
Tests in variance analysis.
Appendix A. Datasets.
Appendix B. Tables.