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by William Mendenhall, Robert J. Beaver and Barbara M. Beaver

ISBN13: 978-0534357788

ISBN10: 0534357784

Edition: 10TH 99

Copyright: 1999

Publisher: Duxbury Press

Published: 1999

International: No

ISBN10: 0534357784

Edition: 10TH 99

Copyright: 1999

Publisher: Duxbury Press

Published: 1999

International: No

"This edition represents a significant improvement and modernization of a text which was already the best of its kind. The authors have done a wonderful job, particularly with integrating a computerized statistical tool. I commend them."

Michael L. Donnell, George Washington University

"I always felt comfortable with the examples used in the textbook because they are usually real-life data. . . .The Tenth Edition brought a welcome change by adding a few 'feel-at-home' type of problems, which help students' understanding of the concepts they are about to learn at first. This is a great idea. I believe in using examples whose contents are familiar (easy to understand) to students so that they can relate themselves to the problem at hand. . . . I would adopt the textbook upon completion."

Yukiko Niiro, Gettysburg College

This new edition has been expanded using state-of-the-art MINITAB graphics to reflect current trends in descriptive data analysis. Much of the text has been rewritten in response to student requests to make the language and style more readable and user friendly. This classic now features a more graphic approach to modernize it, and more emphasis on the uses of the computer in statistics is also a focus for this edition. Inference making, crystal-clear explanations, and well-honed exercises with raw data sets continue to be the hallmarks for the book. Authors Bill Mendenhall and Bob Beaver are joined by Barbara Beaver as coauthor for this revision. In keeping with the evolution of statistics as a discipline and the technology it now utilizes, the authors use the computational and graphical shortcuts provided by the computer to allow more time for a discussion of statistical reasoning and the interpretation of results. They have retained the statistical accuracy and integrity of this classical text, while providing students with the most innovative techniques available as we approach the millennium.

- An increase in the use of computer-generated graphics and a simplification of explanations make the material even more accessible.
- A new Chapter 3 describes bivariate data. The chapter introduces correlation coefficient and the regression line as descriptive tools for bivariate data. The placement of this chapter makes a smooth transition from univariate to bivariate data for instructors who choose to cover this topic.
- The Analysis of Variance chapter now precedes the chapters on regression analysis. This allows the authors to include the Analysis of Variance for a regression analysis in their discussion. This chapter now include an introduction to factorial experiments.
- In the chapters on Analysis of Variance, linear formulas are presented in the main text body along with computer printouts rather than at the ends of chapters. Residual plots and their use in checking model assumptions receive more emphasis in these chapters.
- Sections at the ends of chapters called "About MINITAB" include instructions for generating MINITAB output with discussions that refer to visual screen captures from MINITAB 12 for Windows. They guide students through a "mini-lab", demonstrating the use of MINITAB for the key statistical techniques presented in that chapter.
- Each chapter shows and interprets MINITAB output and graphs.
- A new section, "Key Concepts and Formulas," concludes each chapter as appropriate. They are useful for students as a quick review for exams.
- The concept of statistical significance and p-values for a statistical test of hypothesis is presented at the outset of Chapter 10 and receives more emphasis throughout the discussion. This allows the student to use p-values interchangeably with the critical value approach to testing throughout the remainder of the text.
- Exercises include more raw data sets with a greater emphasis on the interpretation of results. More exercises are included that require written analytical responses from the students.
- Packaged with each copy of the text is a CD-ROM for the student that includes the PowerPoint lecture presentation that is provided to instructors, a tutorial program that offers practice exercises with immediate feedback, a simulation component, and the data sets for the problems in the text.
- The framework of statistical inference as the objective of statistics is now enhanced with graphics and computer usage.
- An accurate, clear execution of explanations, examples, and excercises provide a solid learning experience.
- Examples and exercises using real data sets help students learn how to analyze and present the results of statistical surveys and experiments.

Author Bio

**Mendenhall, William : **

**Beaver, Robert J. : University of California-Riverside **

**Beaver, Barbara M. : University of California-Riverside **

**1. DESCRIBING DATA WITH GRAPHS. **

Variables and Data.

Types of Variables.

Graphs for Categorical Data.

Graphs for Quantitative.

Relative Frequency Histograms.

Key Concepts.

About MINITAB--Introduction to MINITAB.

Case Study.

How Is your Blood Pressure?

**2. DESCRIBING DATA WITH NUMERICAL MEASURES. **

Describing a Set of Data with Numerical Measures.

Measures of Center.

Measures of Variability.

On the Practical Significance of the Standard Deviation.

A Check on the Calculation of s.

Measures of Relative Standing.

The Box Plot.

Key Concepts and Formulas.

About MINITAB--Numerical Descriptive Measures.

Case Study: The Boys of Summer.

**3. DESCRIBING BIVARIATE DATA. **

Bivariate Data.

Graphs for Qualitative Variables.

Scatterplots for Two Quantitative Variables.

Numerical Measures for Quantitative Bivariate Data.

Key Concepts.

About MINITAB--Describing Bivariate Data .

Case Study: Do You Think Your Dishes Are Really Clean? .

**4. PROBABILITY AND PROBABILITY DISTRIBUTIONS. **

The Role of Probability in Statistics.

Events and the Sample Space.

Calculating Probabilities Using Simple Events.

Useful Counting Rules (Optional).

Event Composition and Event Relations.

Conditional Probability and Independence.

Bayes' Rule (Optional).

Discrete Random Variables and Their Probability Distributions.

Key Concepts and Formulas.

About MINITAB--Discrete Probability Distributions.

Case Study: Probability and Decision Making in the Congo.

**5. SEVERAL USEFUL DISCRETE DISTRIBUTIONS. **

Introduction.

The Binomial Probability Distribution.

The Poisson Probability Distribution.

The Hypergeometric Probability Distribution.

Key Concepts and Formulas.

About MINITAB--Binomial and Poisson Probabilities.

Case Study: A Mystery: Cancers Near a Reactor.

**6. THE NORMAL PROBABILITY DISTRIBUTION. **

Probability Distributions for Continuous Random Variables.

The Normal Probability Distribution.

Tabulated Areas of the Normal Probability Distribution.

The Normal Approximation to the Binomial Probability Distribution (Optional).

Key Concepts and Formulas. About MINITAB--Normal Probabilities.

Case Study: The Long and Short of It.

**7. SAMPLING DISTRIBUTIONS. **

Introduction.

Sampling Plans and Experimental Designs.

Statistics and Sampling Distributions.

The Central Limit Theorem.

The Sampling Distribution of the Sample Mean.

The Sampling Distribution of the Sample Proportion.

A Sampling Application: Statistical Process Control (Optional).

Key Concepts and Formulas.

About MINITAB--The Central Limit Theorem at Work.

Case Study: Sampling the Roulette at Monte Carlo.

**8. LARGE--SAMPLE ESTIMATION. **

Where We've Been.

Where We're Going--Statistical Inference.

Types of Estimators.

Point Estimation.

Interval Estimation.

Estimating the Difference Between Two Population Means.

Estimating the Difference Between Two Binomial Proportions.

One--Sided Confidence Bounds.

Choosing the Sample Size.

Key Concepts and Formulas.

Case Study: How Reliable is That Poll?

**9. LARGE--SAMPLE TESTS OF HYPOTHESES. **

Testing Hypotheses About Population Parameters.

A Statistical Test of Hypothesis.

A Large--Sample Test about a Population Mean.

A Large--Sample Test of Hypothesis for the Difference in Two Population Means.

A Large--Sample Test of Hypothesis for a Binomial Proportion.

A Large--Sample Test of Hypothesis for the Difference in Two Binomial. Proportions.

Key Concepts and Formulas.

Case Study: An Aspirin a Day?

**10. INFERENCE FROM SMALL SAMPLES. **

Introduction.

Student's Distribution.

Small--Sample Inferences Concerning a Population Mean.

Small--Sample Inferences for the Difference Between Two Population Means: Independent Random Samples.

Small--Sample Inferences for the Difference Between Two Means: A Paired Difference Test. Inferences Concerning a Population Variance.

Comparing Two Population Variances.

Revisiting the Small Sample Assumptions.

Key Concepts and Formulas.

About MINITAB--Small--Sample Testing and Estimation.

Case Study: How Would You Like a Four--Day Work Week? .

**11. THE ANALYSIS OF VARIANCE . **

The Design of an Experiment.

What is an Analysis of Variance? The Assumptions for an Analysis of Variance.

The Completely Randomized Design: A One--Way Classification.

The Analysis of Variance for a Completely Randomized Design.

Ranking Population Means.

The Randomized Block Design: A Two--Way Classification.

The Analysis of Variance for a Randomized Block Design.

The a x b Factorial Experiment: A Two--Way Classification.

The Analysis of Variance for an a x b Factorial Experiment.

Revisiting the Analysis of Variance Assumptions.

A Brief Summary.

Key Concepts and Formulas. About MINITAB--Analysis of Variance Procedures.

Case Study: "A Fine Mess".

**12. LINEAR REGRESSION AND CORRELATION. **

Introduction.

A Simple Linear Probabilistic Model.

The Method of Least Squares.

An Analysis of Variance for Linear Regression.

Testing the Usefulness of the Linear Regression Model.

Estimation and Prediction Using the Fitted Line.

Revisiting the Regression Assumptions.

Correlation Analysis.

Key Concepts and Formulas.

About MINITAB--Linear Regression Procedures.

Case Study: Is Your Car "Made in the U.S.A.? "

**13. MULTIPLE REGRESSION ANALYSIS. **

Introduction.

The Multiple Regression Model.

A Multiple Regression Analysis.

A Polynomial Regression Model.

Using Quantitative and Qualitative Predictor Variables in a Regression Model.

Testing Sets of Regression Coefficients.

Interpreting Residual Plots.

Stepwise Regression Analysis.

Misinterpreting a Regression Analysis.

Steps to Follow When Building a Multiple Regression Model.

Key Concepts and Formulas.

About MINITAB--Multiple Regression Procedures.

Case Study: "Made in the U.S. A.-- Another Look".

**14. ANALYSIS OF CATEGORICAL DATA. **

A Description of the Experiment.

Pearson's Chi--Square Statistic.

Testing Specified Cell Probabilities: The Goodness--of--Fit Test.

Contingency Tables: A Two--Way Classification.

Comparing Several Multinomial Populations: A Two--Way Classification with Fixed Row or Column Totals.

The Equivalence of Statistical Tests.

Other Applications.

Key Concepts and Formulas.

About MINITAB--The Chi--Square Test.

Case Study: Can a Marketing Approach Improve Library Services?

**15. NONPARAMETRIC STATISTICS. **

Introduction.

The Wilcoxon Rank Sum Test: Independent Random Samples.

The Sign Test for a Paired Experiment.

A Comparison of Statistical Tests.

The Wilcoxon Signed--Rank Test for a Paired Experiment.

The Kruskal--Wallis H Test for Completely Randomized Designs.

The Friedman F Test for Randomized Block Designs.

Rank Correlation Coefficient.

Summary.

Key Concepts and Formulas.

About MINITAB--Nonparametric Procedures.

Case Study: How's Your Cholesterol Level?

**Other Editions for Introduction to Probability and Statistics / With CD**

William Mendenhall, Robert J. Beaver and Barbara M. Beaver

ISBN13: 978-0534357788ISBN10: 0534357784

Edition: 10TH 99

Copyright: 1999

Publisher: Duxbury Press

Published: 1999

International: No

"This edition represents a significant improvement and modernization of a text which was already the best of its kind. The authors have done a wonderful job, particularly with integrating a computerized statistical tool. I commend them."

Michael L. Donnell, George Washington University

"I always felt comfortable with the examples used in the textbook because they are usually real-life data. . . .The Tenth Edition brought a welcome change by adding a few 'feel-at-home' type of problems, which help students' understanding of the concepts they are about to learn at first. This is a great idea. I believe in using examples whose contents are familiar (easy to understand) to students so that they can relate themselves to the problem at hand. . . . I would adopt the textbook upon completion."

Yukiko Niiro, Gettysburg College

This new edition has been expanded using state-of-the-art MINITAB graphics to reflect current trends in descriptive data analysis. Much of the text has been rewritten in response to student requests to make the language and style more readable and user friendly. This classic now features a more graphic approach to modernize it, and more emphasis on the uses of the computer in statistics is also a focus for this edition. Inference making, crystal-clear explanations, and well-honed exercises with raw data sets continue to be the hallmarks for the book. Authors Bill Mendenhall and Bob Beaver are joined by Barbara Beaver as coauthor for this revision. In keeping with the evolution of statistics as a discipline and the technology it now utilizes, the authors use the computational and graphical shortcuts provided by the computer to allow more time for a discussion of statistical reasoning and the interpretation of results. They have retained the statistical accuracy and integrity of this classical text, while providing students with the most innovative techniques available as we approach the millennium.

- An increase in the use of computer-generated graphics and a simplification of explanations make the material even more accessible.
- A new Chapter 3 describes bivariate data. The chapter introduces correlation coefficient and the regression line as descriptive tools for bivariate data. The placement of this chapter makes a smooth transition from univariate to bivariate data for instructors who choose to cover this topic.
- The Analysis of Variance chapter now precedes the chapters on regression analysis. This allows the authors to include the Analysis of Variance for a regression analysis in their discussion. This chapter now include an introduction to factorial experiments.
- In the chapters on Analysis of Variance, linear formulas are presented in the main text body along with computer printouts rather than at the ends of chapters. Residual plots and their use in checking model assumptions receive more emphasis in these chapters.
- Sections at the ends of chapters called "About MINITAB" include instructions for generating MINITAB output with discussions that refer to visual screen captures from MINITAB 12 for Windows. They guide students through a "mini-lab", demonstrating the use of MINITAB for the key statistical techniques presented in that chapter.
- Each chapter shows and interprets MINITAB output and graphs.
- A new section, "Key Concepts and Formulas," concludes each chapter as appropriate. They are useful for students as a quick review for exams.
- The concept of statistical significance and p-values for a statistical test of hypothesis is presented at the outset of Chapter 10 and receives more emphasis throughout the discussion. This allows the student to use p-values interchangeably with the critical value approach to testing throughout the remainder of the text.
- Exercises include more raw data sets with a greater emphasis on the interpretation of results. More exercises are included that require written analytical responses from the students.
- Packaged with each copy of the text is a CD-ROM for the student that includes the PowerPoint lecture presentation that is provided to instructors, a tutorial program that offers practice exercises with immediate feedback, a simulation component, and the data sets for the problems in the text.
- The framework of statistical inference as the objective of statistics is now enhanced with graphics and computer usage.
- An accurate, clear execution of explanations, examples, and excercises provide a solid learning experience.
- Examples and exercises using real data sets help students learn how to analyze and present the results of statistical surveys and experiments.

Author Bio

**Mendenhall, William : **

**Beaver, Robert J. : University of California-Riverside **

**Beaver, Barbara M. : University of California-Riverside **

Table of Contents

**1. DESCRIBING DATA WITH GRAPHS. **

Variables and Data.

Types of Variables.

Graphs for Categorical Data.

Graphs for Quantitative.

Relative Frequency Histograms.

Key Concepts.

About MINITAB--Introduction to MINITAB.

Case Study.

How Is your Blood Pressure?

**2. DESCRIBING DATA WITH NUMERICAL MEASURES. **

Describing a Set of Data with Numerical Measures.

Measures of Center.

Measures of Variability.

On the Practical Significance of the Standard Deviation.

A Check on the Calculation of s.

Measures of Relative Standing.

The Box Plot.

Key Concepts and Formulas.

About MINITAB--Numerical Descriptive Measures.

Case Study: The Boys of Summer.

**3. DESCRIBING BIVARIATE DATA. **

Bivariate Data.

Graphs for Qualitative Variables.

Scatterplots for Two Quantitative Variables.

Numerical Measures for Quantitative Bivariate Data.

Key Concepts.

About MINITAB--Describing Bivariate Data .

Case Study: Do You Think Your Dishes Are Really Clean? .

**4. PROBABILITY AND PROBABILITY DISTRIBUTIONS. **

The Role of Probability in Statistics.

Events and the Sample Space.

Calculating Probabilities Using Simple Events.

Useful Counting Rules (Optional).

Event Composition and Event Relations.

Conditional Probability and Independence.

Bayes' Rule (Optional).

Discrete Random Variables and Their Probability Distributions.

Key Concepts and Formulas.

About MINITAB--Discrete Probability Distributions.

Case Study: Probability and Decision Making in the Congo.

**5. SEVERAL USEFUL DISCRETE DISTRIBUTIONS. **

Introduction.

The Binomial Probability Distribution.

The Poisson Probability Distribution.

The Hypergeometric Probability Distribution.

Key Concepts and Formulas.

About MINITAB--Binomial and Poisson Probabilities.

Case Study: A Mystery: Cancers Near a Reactor.

**6. THE NORMAL PROBABILITY DISTRIBUTION. **

Probability Distributions for Continuous Random Variables.

The Normal Probability Distribution.

Tabulated Areas of the Normal Probability Distribution.

The Normal Approximation to the Binomial Probability Distribution (Optional).

Key Concepts and Formulas. About MINITAB--Normal Probabilities.

Case Study: The Long and Short of It.

**7. SAMPLING DISTRIBUTIONS. **

Introduction.

Sampling Plans and Experimental Designs.

Statistics and Sampling Distributions.

The Central Limit Theorem.

The Sampling Distribution of the Sample Mean.

The Sampling Distribution of the Sample Proportion.

A Sampling Application: Statistical Process Control (Optional).

Key Concepts and Formulas.

About MINITAB--The Central Limit Theorem at Work.

Case Study: Sampling the Roulette at Monte Carlo.

**8. LARGE--SAMPLE ESTIMATION. **

Where We've Been.

Where We're Going--Statistical Inference.

Types of Estimators.

Point Estimation.

Interval Estimation.

Estimating the Difference Between Two Population Means.

Estimating the Difference Between Two Binomial Proportions.

One--Sided Confidence Bounds.

Choosing the Sample Size.

Key Concepts and Formulas.

Case Study: How Reliable is That Poll?

**9. LARGE--SAMPLE TESTS OF HYPOTHESES. **

Testing Hypotheses About Population Parameters.

A Statistical Test of Hypothesis.

A Large--Sample Test about a Population Mean.

A Large--Sample Test of Hypothesis for the Difference in Two Population Means.

A Large--Sample Test of Hypothesis for a Binomial Proportion.

A Large--Sample Test of Hypothesis for the Difference in Two Binomial. Proportions.

Key Concepts and Formulas.

Case Study: An Aspirin a Day?

**10. INFERENCE FROM SMALL SAMPLES. **

Introduction.

Student's Distribution.

Small--Sample Inferences Concerning a Population Mean.

Small--Sample Inferences for the Difference Between Two Population Means: Independent Random Samples.

Small--Sample Inferences for the Difference Between Two Means: A Paired Difference Test. Inferences Concerning a Population Variance.

Comparing Two Population Variances.

Revisiting the Small Sample Assumptions.

Key Concepts and Formulas.

About MINITAB--Small--Sample Testing and Estimation.

Case Study: How Would You Like a Four--Day Work Week? .

**11. THE ANALYSIS OF VARIANCE . **

The Design of an Experiment.

What is an Analysis of Variance? The Assumptions for an Analysis of Variance.

The Completely Randomized Design: A One--Way Classification.

The Analysis of Variance for a Completely Randomized Design.

Ranking Population Means.

The Randomized Block Design: A Two--Way Classification.

The Analysis of Variance for a Randomized Block Design.

The a x b Factorial Experiment: A Two--Way Classification.

The Analysis of Variance for an a x b Factorial Experiment.

Revisiting the Analysis of Variance Assumptions.

A Brief Summary.

Key Concepts and Formulas. About MINITAB--Analysis of Variance Procedures.

Case Study: "A Fine Mess".

**12. LINEAR REGRESSION AND CORRELATION. **

Introduction.

A Simple Linear Probabilistic Model.

The Method of Least Squares.

An Analysis of Variance for Linear Regression.

Testing the Usefulness of the Linear Regression Model.

Estimation and Prediction Using the Fitted Line.

Revisiting the Regression Assumptions.

Correlation Analysis.

Key Concepts and Formulas.

About MINITAB--Linear Regression Procedures.

Case Study: Is Your Car "Made in the U.S.A.? "

**13. MULTIPLE REGRESSION ANALYSIS. **

Introduction.

The Multiple Regression Model.

A Multiple Regression Analysis.

A Polynomial Regression Model.

Using Quantitative and Qualitative Predictor Variables in a Regression Model.

Testing Sets of Regression Coefficients.

Interpreting Residual Plots.

Stepwise Regression Analysis.

Misinterpreting a Regression Analysis.

Steps to Follow When Building a Multiple Regression Model.

Key Concepts and Formulas.

About MINITAB--Multiple Regression Procedures.

Case Study: "Made in the U.S. A.-- Another Look".

**14. ANALYSIS OF CATEGORICAL DATA. **

A Description of the Experiment.

Pearson's Chi--Square Statistic.

Testing Specified Cell Probabilities: The Goodness--of--Fit Test.

Contingency Tables: A Two--Way Classification.

Comparing Several Multinomial Populations: A Two--Way Classification with Fixed Row or Column Totals.

The Equivalence of Statistical Tests.

Other Applications.

Key Concepts and Formulas.

About MINITAB--The Chi--Square Test.

Case Study: Can a Marketing Approach Improve Library Services?

**15. NONPARAMETRIC STATISTICS. **

Introduction.

The Wilcoxon Rank Sum Test: Independent Random Samples.

The Sign Test for a Paired Experiment.

A Comparison of Statistical Tests.

The Wilcoxon Signed--Rank Test for a Paired Experiment.

The Kruskal--Wallis H Test for Completely Randomized Designs.

The Friedman F Test for Randomized Block Designs.

Rank Correlation Coefficient.

Summary.

Key Concepts and Formulas.

About MINITAB--Nonparametric Procedures.

Case Study: How's Your Cholesterol Level?

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**Other Editions for Introduction to Probability and Statistics / With CD**