Warren Beatty, PhD, was Professor of Management (retired) at the University of South Alabama College of Business, where he developed the Department of Management's curriculum on Business Decisions.  During his tenure there, Professor Beatty was central to the university's development and usage of the Statistical Package for the Social Sciences (SPSS) and of the Statistical Analysis System (SAS).  He received a B.S. in Statistics from Mississippi State University, an MBA from the University of South Alabama, and a Ph.D. in Quantitative Management and Decision Making (with a minor in Statistics) from Florida State University.  Professor Beatty has authored over forty refereed journal articles and was a consultant to many Gulf Coast businesses and governments for over thirty years in the areas of computer selection and implementation, statistics and its use in analysis and decision support, and the decision process itself. 

Preface: Purpose, Intended Audience, Scope

The Decision Making Process: A brief look at decision-making itself

1) Introduction to Variables; and an Introduction to Sampling, Sampling Error, and Margin of Error 

2) Introduction to Probability, the Foundation of Statistics, and to Binomial Probability

Section 2: The NonParametric Statistics Tools

3) One Population Hypothesis Testing: Nominal - Runs Test, Sign Test; Ordinal, Interval - Wilcoxon Signed Rank Test

4) Two Population Hypothesis Testing: Nominal - Sign Test, Chi-Square Contingency Table; Ordinal, Interval - Wilcoxon Signed Rank Test, Mann-Whitney U Test

6) Association: Nominal - Chi-Square Test of Association; Ordinal, Interval - Spearman's Rank Correlation

Section 3: Appendices and Miscellaneous

Appendix of Tables Used

References for Chapters 3-6

OpenOffice Help: OpenOffice Help and Compatibility with Excel Statistics Functions

Spreadsheet Tutorial: A Quick & Dirty Spreadsheet Tutorial

This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides the reader with a set of tools that can be used to validly analyze all data, regardless of type. Through numerous examples and exercises, this book explains why nonparametric statistics will lead to better decisions and how they are used to reach a decision, with a wide array of business applications. Online resources include exercise data, spreadsheets, and solutions.