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by Ron Larson and Elizabeth Farber

Edition: 4TH 09Copyright: 2009

Publisher: Prentice Hall, Inc.

Published: 2009

International: No

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For algebra-based Introductory Statistics courses. Offering the most accessible approach to statistics, with a strong visual/graphical emphasis, the book offers a vast number of examples on the premise that students learn best by ''doing''. The fourth edition features many updates and revisions that place increased emphasis on interpretation of results and critical thinking in addition to calculations. This emphasis on ''statistical literacy'' is reflective of the GAISE recommendations.

(NOTE: Each chapter begins with Where Yoursquo;ve Been and Where You're Going sections and concludes with Uses and Abuses, Chapter Summary, Review Exercises, Chapter Quiz, Real Statistics-Real Decisions-Putting it all Together, and Technology sections.)

1. Introduction to Statistics. An Overview of Statistics. Data Classification. Case Study: Rating Television Shows in the United States. Experimental Design.

2. Descriptive Statistics. Frequency Distributions and Their Graphs. More Graphs and Displays. Measures of Central Tendency. Measures of Variation. Case Study: Earnings of Athletes. Measures of Position.

3. Probability. Basic Concepts of Probability. Conditional Probability and the Multiplication Rule. The Addition Rule. Case Study: Probability and Parking Lot Strategies. Additional Topics in Probability and Counting.

4. Discrete Probability Distributions. Probability Distributions. Binomial Distributions. Case Study: Binomial Distribution of Airplane Accidents. More Discrete Probability Distributions.

5. Normal Probability Distributions. Introduction to Normal Distributions and the Standard Normal Distribution. Normal Distributions: Finding Probabilities. Normal Distributions: Finding Values. Case Study: Birth Weights in America. Sampling Distributions and The Central Limit Theorem. Normal Approximations to Binomial Distributions.

6. Confidence Intervals. Confidence Intervals for the Mean (Large Samples). Case Study: Shoulder Heights of Appalachian Black Bears. Confidence Intervals for the Mean (Small Samples). Confidence Intervals for Population Proportions. Confidence Intervals for Variance and Standard Deviation.

7. Hypothesis Testing with One Sample Introduction to Hypothesis Testing. Hypothesis Testing for the Mean (Large Samples). Case Study: Human Body Temperature: What's Normal? Hypothesis Testing for the Mean (Small Samples). Hypothesis Testing for Proportions. Hypothesis Testing for the Variance and Standard Deviation.

8. Hypothesis Testing with Two Samples. Testing the Difference Between Means (Large Independent Samples). Case Study: Oatbran and Cholesterol Level. Testing the Difference Between Means (Small Independent Samples). Testing the Difference Between Means (Dependent Samples). Testing the Difference Between Proportions.

9. Correlation and Regression. Correlation. Linear Regression. Case Study: Correlation of Body Measurements. Measures of Regression and Prediction Intervals. Multiple Regression.

10. Chi-Square Tests and the F-Distribution. Goodness of Fit. Independence. Case Study: Traffic Safety Facts. Comparing Two Variances. Analysis of Variance.

11. Nonparametric Tests. The Sign Test. The Wilcoxon Tests. Case Study: Health and Nutrition. The Kruskal-Wallis Test. Rank Correlation. Runs Test.

Appendix A.

Summary

For algebra-based Introductory Statistics courses. Offering the most accessible approach to statistics, with a strong visual/graphical emphasis, the book offers a vast number of examples on the premise that students learn best by ''doing''. The fourth edition features many updates and revisions that place increased emphasis on interpretation of results and critical thinking in addition to calculations. This emphasis on ''statistical literacy'' is reflective of the GAISE recommendations.

Table of Contents

(NOTE: Each chapter begins with Where Yoursquo;ve Been and Where You're Going sections and concludes with Uses and Abuses, Chapter Summary, Review Exercises, Chapter Quiz, Real Statistics-Real Decisions-Putting it all Together, and Technology sections.)

1. Introduction to Statistics. An Overview of Statistics. Data Classification. Case Study: Rating Television Shows in the United States. Experimental Design.

2. Descriptive Statistics. Frequency Distributions and Their Graphs. More Graphs and Displays. Measures of Central Tendency. Measures of Variation. Case Study: Earnings of Athletes. Measures of Position.

3. Probability. Basic Concepts of Probability. Conditional Probability and the Multiplication Rule. The Addition Rule. Case Study: Probability and Parking Lot Strategies. Additional Topics in Probability and Counting.

4. Discrete Probability Distributions. Probability Distributions. Binomial Distributions. Case Study: Binomial Distribution of Airplane Accidents. More Discrete Probability Distributions.

5. Normal Probability Distributions. Introduction to Normal Distributions and the Standard Normal Distribution. Normal Distributions: Finding Probabilities. Normal Distributions: Finding Values. Case Study: Birth Weights in America. Sampling Distributions and The Central Limit Theorem. Normal Approximations to Binomial Distributions.

6. Confidence Intervals. Confidence Intervals for the Mean (Large Samples). Case Study: Shoulder Heights of Appalachian Black Bears. Confidence Intervals for the Mean (Small Samples). Confidence Intervals for Population Proportions. Confidence Intervals for Variance and Standard Deviation.

7. Hypothesis Testing with One Sample Introduction to Hypothesis Testing. Hypothesis Testing for the Mean (Large Samples). Case Study: Human Body Temperature: What's Normal? Hypothesis Testing for the Mean (Small Samples). Hypothesis Testing for Proportions. Hypothesis Testing for the Variance and Standard Deviation.

8. Hypothesis Testing with Two Samples. Testing the Difference Between Means (Large Independent Samples). Case Study: Oatbran and Cholesterol Level. Testing the Difference Between Means (Small Independent Samples). Testing the Difference Between Means (Dependent Samples). Testing the Difference Between Proportions.

9. Correlation and Regression. Correlation. Linear Regression. Case Study: Correlation of Body Measurements. Measures of Regression and Prediction Intervals. Multiple Regression.

10. Chi-Square Tests and the F-Distribution. Goodness of Fit. Independence. Case Study: Traffic Safety Facts. Comparing Two Variances. Analysis of Variance.

11. Nonparametric Tests. The Sign Test. The Wilcoxon Tests. Case Study: Health and Nutrition. The Kruskal-Wallis Test. Rank Correlation. Runs Test.

Appendix A.

Publisher Info

Publisher: Prentice Hall, Inc.

Published: 2009

International: No

Published: 2009

International: No

(NOTE: Each chapter begins with Where Yoursquo;ve Been and Where You're Going sections and concludes with Uses and Abuses, Chapter Summary, Review Exercises, Chapter Quiz, Real Statistics-Real Decisions-Putting it all Together, and Technology sections.)

1. Introduction to Statistics. An Overview of Statistics. Data Classification. Case Study: Rating Television Shows in the United States. Experimental Design.

2. Descriptive Statistics. Frequency Distributions and Their Graphs. More Graphs and Displays. Measures of Central Tendency. Measures of Variation. Case Study: Earnings of Athletes. Measures of Position.

3. Probability. Basic Concepts of Probability. Conditional Probability and the Multiplication Rule. The Addition Rule. Case Study: Probability and Parking Lot Strategies. Additional Topics in Probability and Counting.

4. Discrete Probability Distributions. Probability Distributions. Binomial Distributions. Case Study: Binomial Distribution of Airplane Accidents. More Discrete Probability Distributions.

5. Normal Probability Distributions. Introduction to Normal Distributions and the Standard Normal Distribution. Normal Distributions: Finding Probabilities. Normal Distributions: Finding Values. Case Study: Birth Weights in America. Sampling Distributions and The Central Limit Theorem. Normal Approximations to Binomial Distributions.

6. Confidence Intervals. Confidence Intervals for the Mean (Large Samples). Case Study: Shoulder Heights of Appalachian Black Bears. Confidence Intervals for the Mean (Small Samples). Confidence Intervals for Population Proportions. Confidence Intervals for Variance and Standard Deviation.

7. Hypothesis Testing with One Sample Introduction to Hypothesis Testing. Hypothesis Testing for the Mean (Large Samples). Case Study: Human Body Temperature: What's Normal? Hypothesis Testing for the Mean (Small Samples). Hypothesis Testing for Proportions. Hypothesis Testing for the Variance and Standard Deviation.

8. Hypothesis Testing with Two Samples. Testing the Difference Between Means (Large Independent Samples). Case Study: Oatbran and Cholesterol Level. Testing the Difference Between Means (Small Independent Samples). Testing the Difference Between Means (Dependent Samples). Testing the Difference Between Proportions.

9. Correlation and Regression. Correlation. Linear Regression. Case Study: Correlation of Body Measurements. Measures of Regression and Prediction Intervals. Multiple Regression.

10. Chi-Square Tests and the F-Distribution. Goodness of Fit. Independence. Case Study: Traffic Safety Facts. Comparing Two Variances. Analysis of Variance.

11. Nonparametric Tests. The Sign Test. The Wilcoxon Tests. Case Study: Health and Nutrition. The Kruskal-Wallis Test. Rank Correlation. Runs Test.

Appendix A.