Neil A. Weiss received his Ph.D. from UCLA in 1970 and subsequently accepted an assistant-professor position at Arizona State University (ASU), where he was ultimately promoted to the rank of full professor. Dr. Weiss has taught statistics, probability, and mathematics-from the freshman level to the advanced graduate level-for more than 30 years. In recognition of his excellence in teaching, he received the Dean's Quality Teaching Award from the ASU College of Liberal Arts and Sciences. Dr. Weiss' comprehensive knowledge and experience ensures that his texts are mathematically and statistically accurate, as well as pedagogically sound.In addition to his numerous research publications, Dr. Weiss is the author of A Course in Probability (Addison-Wesley, 2006). He has also authored or coauthored books in finite mathematics, statistics, and real analysis, and is currently working on a new book on applied regression analysis and the analysis of variance. His texts-well known for their precision, readability, and pedagogical excellence-are used worldwide. Dr. Weiss is a pioneer of the integration of statistical software into textbooks and the classroom, first providing such integration over 20 years ago in the book Introductory Statistics (Addison-Wesley, 1982). Weiss and Addison-Wesley continue that pioneering spirit to this day with the inclusion of some of the most comprehensive Web sites in the field.In his spare time, Dr. Weiss enjoys walking, studying and practicing meditation, and playing hold 'em poker. He is married and has two sons.

PrefaceSupplementsTechnology ResourcesData Sources1. The Nature of Statistics 1.1 Statistics Basics 1.2 Simple Random Sampling 1.3 Other Sampling Designs* 1.4 Experimental Designs* 2. Organizing Data 2.1 Variables and Data2.2 Organizing Qualitative Data2.3 Organizing Quantitative Data2.4 Distribution Shapes2.5 Misleading Graphs*3. Descriptive Measures3.1 Measures of Center3.2 Measures of Variation3.3 The Five-Number Summary; Boxplots3.4 Descriptive Measures for Populations; Use of Samples4. Probability Concepts 4.1 Probability Basics 4.2 Events 4.3 Some Rules of Probability 4.4 Contingency Tables; Joint and Marginal Probabilities* 4.5 Conditional Probability* 4.6 The Multiplication Rule; Independence* 4.7 Bayes's Rule* 4.8 Counting Rules* 5. Discrete Random Variables* 5.1 Discrete Random Variables and Probability Distributions* 5.2 The Mean and Standard Deviation of a Discrete Random Variable* 5.3 The Binomial Distribution* 5.4 The Poisson Distribution* 6. The Normal Distribution 6.1 Introducing Normally Distributed Variables 6.2 Areas Under the Standard Normal Curve 6.3 Working with Normally Distributed Variables 6.4 Assessing Normality; Normal Probability Plots 6.5 Normal Approximation to the Binomial Distribution* 7. The Sampling Distribution of the Sample Mean 7.1 Sampling Error; the Need for Sampling Distributions 7.2 The Mean and Standard Deviation of the Sample Mean 7.3 The Sampling Distribution of the Sample Mean 8. Confidence Intervals for One Population Mean 8.1 Estimating a Population Mean 8.2 Confidence Intervals for One Population Mean When � Is Known 8.3 Margin of Error 8.4 Confidence Intervals for One Population Mean When � Is Unknown 9. Hypothesis Tests for One Population Mean 9.1 The Nature of Hypothesis Testing 9.2 Critical-Value Approach to Hypothesis Testing 9.3 P-Value Approach to Hypothesis Testing9.4 Hypothesis Tests for One Population Mean When � Is Known 9.5 Hypothesis Tests for One Population Mean When � Is Unknown 9.6 The Wilcoxon Signed-Rank Test* 9.7 Type II Error Probabilities; Power* 9.8 Which Procedure Should Be Used?* 10. Inferences for Two Population Means 10.1 The Sampling Distribution of the Difference between Two Sample Means for Independent Samples 10.2 Inferences for Two Population Means, Using Independent Samples: Standard Deviations Assumed Equal 10.3 Inferences for Two Population Means, Using Independent Samples: Standard Deviations Not Assumed Equal 10.4 The Mann-Whitney Test* 10.5 Inferences for Two Population Means, Using Paired Samples 10.6 The Paired Wilcoxon Signed-Rank Test* 10.7 Which Procedure Should Be Used?* 11. Inferences for Population Standard Deviations* 11.1 Inferences for One Population Standard Deviation* 11.2 Inferences for Two Population Standard Deviations, Using Independent Samples* 12. Inferences for Population Proportions 12.1 Confidence Intervals for One Population Proportion 12.2 Hypothesis Tests for One Population Proportion 12.3 Inferences for Two Population Proportions 13. Chi-Square Procedures 13.1 The Chi-Square Distribution 13.2 Chi-Square Goodness-of-Fit Test 13.3 Contingency Tables; Association 13.4 Chi-Square Independence Test13.5 Chi-Square Homogeneity Test14. Descriptive Methods in Regression and Correlation 14.1 Linear Equations with One Independent Variable 14.2 The Regression Equation 14.3 The Coefficient of Determination 14.4 Linear Correlation 15. Inferential Methods in Regression and Correlation 15.1 The Regression Model; Analysis of Residuals 15.2 Inferences for the Slope of the Population Regression Line 15.3 Estimation and Prediction 15.4 Inferences in Correlation 15.5 Testing for Normality* 16. Analysis of Variance (ANOVA) 16.1 The F-Distribution 16.2 One-Way ANOVA: The Logic 16.3 One-Way ANOVA: The Procedure 16.4 Multiple Comparisons* 16.5 The Kruskal-Wallis Test* AppendixesAppendix A: Statistical Tables Appendix B: Answers to Selected ExercisesStatistical TablesAnswers to Selected ExercisesIndexPhoto Credits