Rowan University Library owns many electronic resources to assist with understanding statistical concepts. All the resources are available to Rowan students, staff and faculty.

- Introduction to Robust Estimation and Hypothesis Testing by Rand R. WilcoxCall Number: Available onlineISBN: 0123870151Publication Date: 2011-12-141. 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. - Understanding Significance Testing by Lawrence B. MohrCall Number: Available onlineISBN: 9780803935686Publication Date: 1990-02-01Chapter 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. - 100 Statistical Tests by Gopal K. KanjiCall Number: Available OnlineISBN: 9781412923767Publication Date: 2006-08-07This 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.
- Statistical Hypothesis Testing with SAS and R by Dirk Taeger; Sonja KuhntCall Number: Available onlineISBN: 1118762606Publication Date: 2014-01-07Part 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.

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