Statistics: Rowan University Library Resources

1. Introduction
2. A Foundation for Robust Methods
3. Estimating Measures of Location and Scale
4. Confidence Intervals in the One-Sample Case
5. Comparing Two Groups
6. Some Multivariate Methods
7. One-Way and Higher Designs for Independent Groups
8. Comparing Multiple Dependent Groups
9. Correlation and Tests of Independence
10. Robust Regression
11. More Regression Methods.

This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context.

Chapter 1: Introduction
Chapter 2: Interval Estimation
Chapter 3: Some Definitions
Chapter 4: The functions of the test
Chapter 5: The Sampling Distribution
Chapter 6: Significance Testing

Significance testing - a core technique in statistics for hypothesis testing - is introduced in this volume. Mohr first reviews what is meant by sampling and probability distributions and then examines in-depth normal and t-tests of significance. The uses and misuses of significance testing are also explored.

This best-selling resource on statistical tests covers all the most commonly used tests with information on how to calculate and interpret results with simple datasets. Each entry begins with a short summary statement about the test's purpose, and contains details of the test objective, the limitations (or assumptions) involved, a brief outline of the method, a worked example and the numerical calculation.

Part I Introduction;
Chapter 1 Statistical hypothesis testing;
1.1 Theory of statistical hypothesis testing;
1.2 Testing statistical hypothesis with SAS and R;
1.2.1 Programming philosophy of SAS and R;
1.2.2 Testing in SAS and R-An example;
1.2.3 Calculating p-values;
1.3 Presentation of the statistical tests;
Part II Normal Distribution;
Chapter 2 Tests on the mean;
2.1 One-sample tests;
2.1.1 z-test;
2.1.2 t-test;
2.2 Two-sample tests;
2.2.1 Two-sample z-test;
2.2.2 Two-sample pooled t-test;
2.2.3 Welch test;
2.2.4 Paired z-test
2.2.5 Paired t-test
Chapter 3 Tests on the variance;
3.1 One-sample tests;
3.1.1 x2-test on the variance (mean known);
3.1.2 x2-test on the variance (mean unknown);
3.2 Two-sample tests;
3.2.1 Two-sample F-test on variances of two populations;
3.2.2 t-test on variances of two dependent populations;
Part III Binomial Distribution;
Chapter 4 Tests on proportions;
4.1 One-sample tests;
4.1.1 Binomial test;
4.2 Two-sample tests;
4.2.1 z-test for the difference of two proportions (unpooled variances);
4.2.2 z-test for the equality between two proportions (pooled variances)
4.3 K-sample tests
4.3.1 K-sample binomial test;
Part IV Other Distributions;
Chapter 5 Poisson distribution;
5.1 Tests on the Poisson parameter;
5.1.1 z-test on the Poisson parameter;
5.1.2 Exact test on the Poisson parameter;
5.1.3 z-test on the difference between two Poisson parameters;
Chapter 6 Exponential distribution;
6.1 Test on the parameter of an exponential distribution;
6.1.1 z-test on the parameter of an exponential distribution;
Part V Correlation;
Chapter 7 Tests on association;
7.1 One-sample tests
7.1.1 Pearson's product moment correlation coefficient
7.1.2 Spearman's rank correlation coefficient;
7.1.3 Partial correlation;
7.2 Two-sample tests;
7.2.1 z-test for two correlation coefficients (independent populations);
Part VI Nonparametric Tests;
Chapter 8 Tests on location;
8.1 One-sample tests;
8.1.1 Sign test;
8.1.2 Wilcoxon signed-rank test;
8.2 Two-sample tests;
8.2.1 Wilcoxon rank-sum test (Mann-Whitney U test);
8.2.2 Wilcoxon matched-pairs signed-rank test;
8.3 K-sample tests;
8.3.1 Kruskal-Wallis test;
Chapter 9 Tests on scale difference
9.1 Two-sample tests
9.1.1 Siegel-Tukey test;
9.1.2 Ansari-Bradley test;
9.1.3 Mood test;
Chapter 10 Other tests;
10.1 Two-sample tests;
10.1.1 Kolmogorov-Smirnov two-sample test (Smirnov test);
Part VII Goodness-of-Fit Tests;
Chapter 11 Tests on normality;
11.1 Tests based on the EDF;
11.1.1 Kolmogorov-Smirnov test (Lilliefors test for normality);
11.1.2 Anderson-Darling test;
11.1.3 Cramér-von Mises test;
11.2 Tests not based on the EDF;
11.2.1 Shapiro-Wilk test;
11.2.2 Jarque-Bera test;
Chapter 12 Tests on other distributions;
12.1 Tests based on the EDF
12.1.1 Kolmogorov-Smirnov test

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.