Introductory Statistics

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.  

Preface   

Supplements List   

Data Sources    

 

Part I: Introduction

 

Chapter 1 The Nature of Statistics

1.1 Statistics Basics

1.2 Simple Random Sampling

1.3 Other Sampling Designs*

1.4 Experimental Designs*

 

Part II: Descriptive Statistics

 

Chapter 2 Organizing Data

2.1 Variables and Data

2.2 Grouping Data 

2.3 Graphs and Charts

2.4 Distribution Shapes

2.5 Misleading Graphs

 

Chapter 3 Descriptive Measures

3.1 Measures of Center

3.2 Measures of Variation 

3.3 The Five-Number Summary; Boxplots

3.4 Descriptive Measures for Populations; Use of Samples

 

Part III: Probability, Random Variables, and Sampling Distributions

 

Chapter 4 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*

 

Chapter 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*

 

Chapter 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*

 

Chapter 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

 

Part IV: Inferential Statistics

 

 Chapter 8 Confidence Intervals for One Population Mean

8.1 Estimating a Population Mean

8.2 Confidence Intervals for One Population Mean When σ is Known

8.3 Margin of Error

8.4 Confidence Intervals for One Population Mean When σ is Unknown

 

Chapter 9 Hypothesis Tests for One Population Mean

9.1 The Nature of Hypothesis Testing

9.2 Terms, Errors, and Hypotheses

9.3 Hypothesis Tests for One Population Mean When σ is Known

9.4 Type II Error Probabilities; Power*

9.5 P-Values

9.6 Hypothesis Tests for One Population Mean When σ is Unknown

9.7 The Wilcoxon Signed-Rank Test*

9.8 Which Procedure Should Be Used?*

 

Chapter 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?*

 

Chapter 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*

 

Chapter 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

 

 Chapter 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 Test

 

Part V: Regression, Correlation, and ANOVA

 

 Chapter 14 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

 

Chapter 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*

 

Chapter 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*

 

Part VI: Multiple Regression and Model Building; Experimental Design and ANOVA (On The WeissStats CD-ROM)

 

Module A Multiple Regression Analysis

A.1 The Multiple Linear Regression Model

A.2 Estimation of the Regression Parameters

A.3 Inferences Concerning the Utility of the Regression Model

A.4 Inferences Concerning the Utility of Particular Predictor Variables

A.5 Confidence Intervals for Mean Response; Prediction Intervals for Response

A.6 Checking Model Assumptions and Residual Analysis

 

Module B Model Building in Regression 

B.1 Transformations to Remedy Model Violations

B.2 Polynomial Regression Model

B.3 Qualitative Predictor Variables

B.4 Multicollinearity

B.5 Model Selection: Stepwise Regression

B.6 Model Selection: All Subsets Regression

B.7 Pitfalls and Warnings

 

Module C Design of Experiments and Analysis of Variance

C.1 Factorial Designs

C.2 Two-Way ANOVA: The Logic

C.3 Two-Way ANOVA: The Procedure

C.4 Two-Way ANOVA: Multiple Comparisons

C.5 Randomized Block Designs

C.6 Randomized Block ANOVA: The Logic

C.7 Randomized Block ANOVA: The Procedure 

C.8 Randomized Block ANOVA: Multiple Comparisons

C.9 Friedman’s Nonparametric Test for the Randomized Block Design*

 

APPENDIXES

Appendix A: Statistical Tables

I. Random numbers

II. Areas under the standard normal curve

III. Normal scores

IV. Values of tα

V. Values of Wα

VI. Values of Mα

VII. Values of χα2  

VIII. Values of Fα 

IX. Critical values for a correlation test for normality

X. Values of q0.01

XI. Values of q0.05

XII. Binomial probabilities 

 

Appendix B Answers to Selected Exercises

Index

Photo Credits

Indexes for Biographical Sketches & Case Studies

 

*indicates an optional section