Ron Larson received his Ph.D. in mathematics from the University of Colorado in 1970. At that time he accepted a position with Penn State University, and he currently holds the rank of professor of mathematics at the university. Larson is the lead author of more than two dozen mathematics textbooks that range from 6th grade through calculus levels. Many of his texts, such as the 10th edition of his calculus text, are leaders in their markets. Larson is also one of the pioneers in the use of multimedia and the Internet to enhance the learning of mathematics. He has authored multimedia programs, extending from the elementary school through calculus levels. Larson is a member of several professional groups and is a frequent speaker at national and regional mathematics meetings.

Many thanks goes to Betsy Farber for her significant contributions to previous editions of the text. Sadly, Betsy passed away in 2013. She received her Bachelor’s degree in mathematics from Penn State University and her Master’s degree in mathematics from the College of New Jersey. Betsy taught all levels of mathematics at Bucks County Community College in Newtown, Pennsylvania for many years. She made many notable contributions throughout her career to developing new ways to make statistics relevant and interesting for both online and traditional students.

I. DESCRIPTIVE STATISTICS

1. Introduction to Statistics

1.1 An Overview of Statistics 

1.2 Data Classification 

1.3 Data Collection and Experimental Design 

2. Descriptive Statistics 

2.1 Frequency Distributions and Their Graphs 

2.2 More Graphs and Displays 

2.3 Measures of Central Tendency 

2.4 Measures of Variation 

2.5 Measures of Position 

II. PROBABILITY AND PROBABILITY DISTRIBUTIONS

3. Probability 

3.1 Basic Concepts of Probability and Counting 

3.2 Conditional Probability and the Multiplication Rule 

3.3 The Addition Rule 

3.4 Additional Topics in Probability and Counting 

4. Discrete Probability Distributions 

4.1 Probability Distributions 

4.2 Binomial Distributions 

4.3 More Discrete Probability Distributions 

5. Normal Probability Distributions 

5.1 Introduction to Normal Distributions and the Standard Normal Distribution 

5.2 Normal Distributions: Finding Probabilities 

5.3 Normal Distributions: Finding Values 

5.4 Sampling Distributions and the Central Limit Theorem 

5.5 Normal Approximations to Binomial Distributions 

III. STATISTICAL INFERENCE

6. Confidence Intervals 

6.1 Confidence Intervals for the Mean (S Known) 

6.2 Confidence Intervals for the Mean (S Unknown) 

6.3 Confidence Intervals for Population Proportions 

6.4 Confidence Intervals for Variance and Standard Deviation 

7. Hypothesis Testing with One Sample 

7.1 Introduction to Hypothesis Testing 

7.2 Hypothesis Testing for the Mean (S Known) 

7.3 Hypothesis Testing for the Mean (S Unknown) 

7.4 Hypothesis Testing for Proportions 

7.5 Hypothesis Testing for Variance and Standard Deviation 

8. Hypothesis Testing with Two Samples
8.1 Testing the Difference Between Means (Independent Samples, S1 and S2 Known)
8.2 Testing the Difference Between Means (Independent Samples, S1 and S2 Unknown)
8.3 Testing the Difference Between Means (Dependent Samples)
8.4 Testing the Difference Between Proportions

IV. MORE STATISTICAL INFERENCE

9. Correlation and Regression 

9.1 Correlation 

9.2 Linear Regression 

9.3 Measures of Regression and Prediction Intervals 

9.4 Multiple Regression 

10. Chi-Square Tests and the F -Distribution 

10.1 Goodness-of-Fit Test 

10.2 Independence 

10.3 Comparing Two Variances 

10.4 Analysis of Variance 

11. Nonparametric Tests (Web Only)*

11.1 The Sign Test

11.2 The Wilcoxon Tests

11.3 The Kruskal-Wallis Test

11.4 Rank Correlation

11.5 The Runs Test

* Available at www.pearson.com/math-stats-resources and in MyLab Statistics.

APPENDICES

Appendix A: Alternative Presentation of the Standard Normal Distribution 

Appendix B: Tables 

Appendix C: Normal Probability Plots 

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