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## Summary

## Author Biography

**Neil A. Weiss** received his Ph.D. from UCLA 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 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.

## Table of Contents

Preface

Course Management Notes (Instructor’s Edition only)

Supplements

Technology Resources

Data Sources

**Part I: Introduction**

**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**

**2. Organizing Data**

2.1 Variables and Data

2.2 Organizing Qualitative Data

2.3 Organizing Quantitative Data

2.4 Distribution Shapes

2.5 Misleading Graphs*

**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

**4. Descriptive Methods in Regression and Correlation **

4.1 Linear Equations with One Independent Variable

4.2 The Regression Equation

4.3 The Coefficient of Determination

4.4 Linear Correlation

**Part III: Probability, Random Variables, and Sampling Distributions**

**5. Probability and Random Variables **

5.1 Probability Basics

5.2 Events

5.3 Some Rules of Probability

5.4 Discrete Random Variables and Probability Distributions*

5.5 The Mean and Standard Deviation of a Discrete Random Variable*

5.6 The Binomial 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

**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**

**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

**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 Testing

9.4 Hypothesis Tests for One Population Mean When σ Is Known

9.5 Hypothesis Tests for One Population Mean When σ Is Unknown

**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 Inferences for Two Population Means, Using Paired Samples

**11. Inferences for Population Proportions **

11.1 Confidence Intervals for One Population Proportion

11.2 Hypothesis Tests for One Population Proportion

11.3 Inferences for Two Population Proportions

**12. Chi-Square Procedures **

12.1 The Chi-Square Distribution

12.2 Chi-Square Goodness-of-Fit Test

12.3 Contingency Tables; Association

12.4 Chi-Square Independence Test

12.5 Chi-Square Homogeneity Test

**13. Analysis of Variance (ANOVA) **

13.1 The *F*-Distribution

13.2 One-Way ANOVA: The Logic

13.3 One-Way ANOVA: The Procedure

**14. Inferential Methods in Regression and Correlation **

14.1 The Regression Model; Analysis of Residuals

14.2 Inferences for the Slope of the Population Regression Line

14.3 Estimation and Prediction

14.4 Inferences in Correlation

**APPENDICES **

**Appendix A: **Statistical Tables

I. Random numbers

II. Areas under the standard normal curve

III. Normal scores

IV. Values of *t _{α} *

V. Values of *χ _{α} *

^{2}

VI. Values of *F _{α} *

VII. Binomial probabilities

**Appendix B Answers to Selected Exercises **

Index

Photo Credits

Indexes for Biographical Sketches & Case Studies

**WeissStats CD-ROM (included with every new textbook)**

**Brief Contents**

*Note: *See the WeissStats CD-ROM ReadMe file for detailed contents.

Applets

Data Sets

DDXL (Excel Add-In)

Detailed *t* and Chi-square Tables

Focus Database

Formulas and Appendix A Tables

Further Topics in Probability

JMP Concept Discovery Modules

Minitab Macros

Technology Basics

TI Programs

*indicates an optional section