Introductory Statistics / Edition 5

Introductory Statistics / Edition 5

by Neil A. Weiss
Pub. Date:
Pearson Education


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Introductory Statistics / Edition 5

With Introductory Statistics, students learn the core statistical concepts in an applied setting, and can access more advanced topics through chapters available on a custom or modular basis. The Fifth Edition is useful and reality-based, employing technological tools such as Minitab Release 12, the TI83 graphing calculator, Excel and the Internet to investigate problems.

Product Details

ISBN-13: 9780201598773
Publisher: Pearson Education
Publication date: 12/28/1998
Edition description: Older Edition
Pages: 1
Product dimensions: 8.20(w) x 9.51(h) x 1.89(d)

About the Author

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.

Table of Contents

(*indicates an optional section).


1. The Nature of Statistics.
Case Study: Top Films of All Time.
Two Kinds of Statistics.
The Technology Center.
Simple Random Sampling.
Other Sampling Designs.
Experimental Designs.


2. Organizing Data.
Case Study: Preventing Infant Mortality.
Variables and Data.
Grouping Data.
Graphs and Charts.
Stem-and-Leaf Diagrams.
Distribution Shapes; Symmetry and Skewness.
Misleading Graphs.

3. Descriptive Measures.
Case Study: New York Yankees Y2K Salaries.
Measures of Center.
The Sample Mean.
Measures of Variation; the Sample Standard Deviation.
The Five-Number Summary; Boxplots.
Descriptive Measures for Populations; Use of Samples.


4. Probability Concepts.
Case Study: The Powerball.
Probability Basics.
Some Rules of Probability.
*Contingency Tables; Joint and Marginal Probabilities.
*Conditional Probability.
*The Multiplication Rule; Independence.
*Bayes's Rule.
*Counting Rules.

5. Discrete Random Variables.
Case Study:Aces Wild on the Sixth at Oak Hill.
*Discrete Random Variables and Probability Distributions.
*The Mean and Standard Deviation of a Discrete Random Variable.
*The Binomial Distribution.
*The Poisson Distribution.

6. The Normal Distribution.
Case Study: Chest Sizes of Scottish Militiamen.
Introducing Normally Distributed Variables.
Areas Under the Standard Normal Curve.
Working With Normally Distributed Variables.
Assessing Normality; Normal Probability Plots.
*Normal Approximation to the Binomial Distribution .

7. The Sampling Distribution of the Sample Mean.
Case Study: The Chesapeake and Ohio Freight Study.
Sampling Error; the Need for Sampling Distributions.
The Mean and Standard Deviation of x.
The Sampling Distribution of the Sample Mean.


8. Confidence Intervals for One Population Mean.
Case Study: The Chips Ahoy! 1,000 Chips Challenge.
Estimating a Population Mean.
Confidence Intervals for One Population Mean When …s Is Known.
Margin of Error.
Confidence Intervals for One Population Mean When …s Is Unknown.

9. Hypothesis Tests for One Population Mean.
Case Study: Sex and Sense of Direction.
The Nature of Hypothesis Testing.
Terms, Errors, and Hypotheses.
Hypothesis Tests for One Population Mean When …s Is Known.
*Type II Error Probabilities; Power.
Hypothesis Tests for One Population Mean When …s Is Unknown.
*The Wilcoxon Signed-Rank Test.
*Which Procedure Should Be Used?

10. Inferences for Two Population Means.
Case Study: Breast Milk and IQ.
The Sampling Distribution of the Difference Between Two Sample Means for Independent Samples.
Inferences for Two Population Means Using Independent Samples: Standard Deviations Assumed Equal.
Inferences for Two Population Means Using Independent Samples: Standard Deviations Not Assumed Equal.
*The Mann-Whitney Test.
Inferences for Two Population Means Using Paired Samples.
*The Paired Wilcoxon Signed-Rank Test.
*Which Procedure Should Be Used?

11. Inferences for Population Standard Deviations.
Case Study: Speaker Woofer Driver Manufacturing.
*Inferences for One Population Standard Deviation.
*Inferences for Two Population Standard Deviations Using Independent Samples.

12. Inferences for Population Proportions.
Case Study: Double-Dipping ATM Fees.
Confidence Intervals for One Population Proportion.
Hypothesis Tests for One Population Proportion.
Inferences for Two Population Proportions Using Independent Samples.

13. Chi-Square Procedures.
Case Study: Road Rage.
The Chi-Square Distribution.
Chi-Square Goodness-Of-Fit Test.
Contingency Tables; Association.
Chi-Square Independence Test.


14. Descriptive Methods in Regression and Correlation.
Case Study: Fat Consumption and Prostate Cancer.
Linear Equations With One Independent Variable.
The Regression Equation.
The Coefficient of Determination.
Linear Correlation.

15. Inferential Methods in Regression and Correlation.
Case Study: Fat Consumption and Prostate Cancer.
The Regression Model; Analysis of Residuals.
Inferences for the Slope of the Population Regression Line.
Estimation and Prediction.
Inferences in Correlation.
*Testing for Normality.

16. Analysis of Variance (Anova).
Case Study: Heavy Drinking Among College Students.
The F-Distribution.
One-Way ANOVA: The Logic.
One-Way ANOVA: The Procedure.
*Multiple Comparisons.
*The Kruskal-Wallis Test.


Module A. Multiple Regression Analysis.
The Multiple Linear Regression Model.
Estimation of the Regression Parameters.
Inferences Concerning the Utility of the Regression Model.
Inferences Concerning the Utility of Particular Predictor Variables.
Confidence Intervals for Mean Response; Prediction.
Intervals for Response.
Checking Model Assumptions and Residual Analysis.

Module B. Model Building in Regression.
Transformations to Remedy Model Violations.
Polynomial Regression Model.
Qualitative Predictor Variables.
Model Selection: Stepwise Regression.
Model Selection: All Subsets Regression.
Pitfalls and Warnings.

Module C. Design Of Experiments and Analysis of Variance.
Factorial Designs.
Two-Way ANOVA: The Logic.
Two-Way ANOVA: The Procedure.
Two-Way ANOVA: Multiple Comparisons.
Randomized Block Designs.
Randomized Block ANOVA: The Logic.
Randomized Block ANOVA: The Procedure.
Randomized Block ANOVA: Multiple Comparisons.
*Friedman's Nonparametric Test for the Randomized Block Design.


Appendix A. Statistical Tables.
Appendix B. Answers To Selected Exercises.


Introductory Statistics, 7e


Using and understanding statistics andstatistical procedures have become required skills in virtually everyprofession and academic discipline. The purpose of this book is to helpstudents grasp basic statistical concepts and techniques, and to present real-life opportunities for applying them.

About This Book

The text is intended for a one- or two-semestercourse and for quarter-system courses as well. Instructors can easily fit thetext to the pace and depth they prefer. Introductory high school algebra is asufficient prerequisite. Although mathematically and statistically sound, theapproach doesn't require students to examine complex concepts such asprobability theory and random variables. Students need only understand basicideas such as percentages and histograms.

Advances in technology and newinsights into the practice of teaching statistics have inspired many of thepedagogical strategies used in the Seventh Edition of IntroductoryStatistics,leading to more emphasis on conceptual understanding and less emphasis oncomputation.

Highlights of the Approach

ASA/MAA-GuidelinesCompliant. We followASA/MAA guidelines to stress the interpretation of statistical results, thecontemporary applications of statistics, and the importance of criticalthinking.

Unique Variable-Centered Approach. By consistent and proper use of theterms variable and population, we unified and clarified the various statistical concepts.

Data Analysis and Exploration. We incorporate an extensive amount ofdata analysis and exploration in the text andexercises. Recognizing that notall readers have access to technology, we provide ample opportunity to analyzeand explore data without the use of a computer or statistical calculator.

Detailed and Careful Explanations. We include every step of explanationthat a typical reader might need. Our guiding principle is to avoid cognitivejumps, making the learning process smooth and enjoyable. We believe thatdetailed and careful explanations result in better understanding.

Emphasis on Application. Weconcentrate on the application of statistical techniques to the analysis ofdata. Although statistical theory has been kept to a minimum, we provide athorough explanation of the rationale for the use of each statisticalprocedure.

Parallel Critical-Value/P-Value Approaches. Through aparallel presentation, the book offers complete flexibility in the coverage ofthe critical-value and P-valueapproaches to hypothesis testing-either one or both approaches can be exploredand compared.

ParallelPresentations of Technology.The book offers complete flexibility in the coverage of technology,which includes options for use of Minitab, Excel, and the TI-83/84 Plus. One or more technologies can beexplored and compared.

New and Hallmark Features

Chapter-Opening Features. Included at the beginning of eachchapter is a general description of the chapter, an explanation of how thechapter relates to the text as a whole, and an outline that lists the sectionsin the chapter. Each chapter opens with a classic or contemporary case studythat highlights the real-world relevance of the material. (Case studies arereviewed and discussed at the end of the chapter.) More than one-third of thecase studies are new or updated.

Real-World Examples.Every concept discussed in the text is illustrated by at least onedetailed example. The examples are based on real-life situations and werechosen for their interest level as well as for their illustrative value.

Interpretation Boxes. This feature presents the meaning and significance of statisticalresults in everyday language. Instead of just obtaining the answers or results,students are shown the importance of interpretation.

What Does It Mean?.This feature, found in the margin at appropriate places, states in"plain English" the meaning of definitions, formulas, and key facts. It is alsoused to summarize various expository discussions.

Data Sets. In most examples and many exercises, we present both raw data and summarystatistics. This practice gives a more realistic view of statistics andprovides an opportunity for students to solve problems by computer orstatistical calculator, if so desired. Hundreds of data sets are included, manyof which are new or updated. All data sets, including large ones, are availablein multiple formats on the WeissStats CD.

Procedure Boxes: Why, When, and How. To help students learnstatistical procedures, we developed easy-to-follow, step-by-step methods forcarrying them out. Each step is highlighted and presented again within theillustrating example. This approach shows how the procedure is applied andhelps students master its steps.

The procedure boxes havebeen reformatted to include the "why, when, and how" of the methods. Usually, aprocedure has a brief identifying title followed by a statement of its purpose(why it's used), the assumptions for its use (when it's used), and the stepsfor applying the procedure (how it's used). The procedures have been combinedinto a new, single split format for ease of use and comparison.

The Technology Center.The in-text coverage of statistical technology includes three of themost popular applications: Minitab, Excel, and the TI-83/84 Plus graphingcalculators. We provide instructions and output for the most recent versions ofthese applications, including Release 14 of Minitab. The Technology Centers areintegrated as optional material.

Computer Simulations. Computer simulations appear in both the text and theexercises. The simulations serve as pedagogical aids for understanding complexconcepts such as sampling distributions.

Exercises. Over 1700 exercises provide current,real-world applications and were constructed from an extensive variety ofarticles in newspapers, magazines, statistical abstracts, journals, and Websites; sources are explicitly cited. The exercises help students learn thematerial and, moreover, show that statistics is a lively and relevant discipline.We updated exercises wherever appropriate and have provided many new ones.Exercises related to optional materials are marked with asterisks unless theentire section is optional.

Most section exercise sets are divided intothree categories. Statistical Concepts and Skillsexercises help students master the skills and concepts explicitly discussed inthe section.

Extending the Concepts and Skills exercises invite students to extend their skills byexamining material not necessarily covered in the text. Exercises thatintroduce new concepts are highlighted in blue.

Using Technologyexercises provide students with an opportunity to apply and interpret thecomputing and statistical capabilities of Minitab, Excel, the TI-83/84 Plus,SPSS, or any other statistical technology.

End-of-Chapter Features:

Chapter Reviews. Each chapter review includes chapterobjectives, a list of Key Terms with page references, and a Review Test to help students reviewand study the chapter. Items related to optional materials are marked withasterisks unless the entire chapter is optional.

Award-Winning Internet Projects. Each chapter includes an InternetProject to engage students in active and collaborative learning throughsimulations, demonstrations, and other activities, and guide them throughapplications by using Internet links to access data and other informationprovided by the vast resources of the World Wide Web. The Internet Projects are featured on the Weiss Web site at or call us at 1-888-777-0463.


First, we want to express our sincereappreciation to all reviewers of previous editions for their many contributionsto the evolution of the book. For this and the previous few editions of thebook, it is our pleasure to thank the following reviewers, whose comments andsuggestions resulted in significant improvements.

James Albert

Bowling Green StateUniversity

Yvonne Brown

Pima Community College

Beth Chance

California Polytechnic StateUniversity

Brant Deppa

Winona State University

Carol DeVille

Louisiana Tech University

Jacqueline Fesq

Raritan Valley CommunityCollege

Richard Gilman

Holy Cross College

Joel Haack

University of Northern Iowa

Susan Herring

Sonoma State University

David Holmes

The College of New Jersey

Satish Iyengar

University of Pittsburgh

Christopher Lacke

Rowan University

Tze-San Lee

Western Illinois University

Ennis Donice McCune

Stephen F. Austin StateUniversity

Jacqueline B. Miller

Drury University

Bernard J. Morzuch

University of Massachusetts,Amherst

Dennis M. O'Brien

University of Wisconsin, LaCrosse

Dwight M. Olson

John Carroll University

JoAnn Paderi

Lourdes College

Melissa Pedone

Valencia Community College

Alan Polansky

Northern Illinois University

Cathy D. Poliak

Northern Illinois University

Kimberley A. Polly

Parkland College

Geetha Ramachandran

California State University

B. Madhu Rao

Bowling Green StateUniversity

Gina F. Reed

Gainesville College

Steven E. Rigdon

Southern Illinois University,Edwardsville

Sharon Ross

Georgia Perimeter College

Edward Rothman

University of Michigan

George W. Schultz

St. Petersburg Jr. College

Arvind Shah

University of South Alabama

Cid Srinivasan

University of Kentucky,Lexington

W. Ed Stephens

McNeese State University

Kathy Taylor

Clackamas Community College

Bill Vaughters

Valencia Community College

Brani Vidakovic

Georgia Institute ofTechnology

Dawn White

California State University,Bakersfield

Marlene Will

Spalding University

Matthew Wood

University of Missouri,Columbia

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