Introductory Statistics / Edition 9

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Weiss offers a comprehensive, traditional approach to introductory statistics, with a heavy emphasis on mathematical reasoning and critical thinking. Weiss's meticulous style offers careful, detailed explanations to ease the learning process. More than 2,600 exercises involve real data from contemporary sources, which promotes active learning and develops readers' ability to think critically with statistics. Introductory Statistics, Ninth Edition, contains parallel presentation of critical-value and p-value approaches to hypothesis testing. This unique design allows both the flexibility to concentrate on one approach or the opportunity for greater depth in comparing the two. The text is suitable for a one- or two-semester course.

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Editorial Reviews

A textbook that can be used in a one-quarter, one-semester, two- quarter, two-semester, or three-quarter course. It provides an understanding of basic statistical concepts and techniques and presents procedures for applying them. The emphasis is on the application of statistical techniques to the analysis of data. Introductory high school algebra is sufficient prerequisite. Annotation c. Book News, Inc., Portland, OR
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Product Details

  • ISBN-13: 9780321691224
  • Publisher: Pearson
  • Publication date: 1/10/2011
  • Edition description: Older Edition
  • Edition number: 9
  • Pages: 896
  • Sales rank: 108,062
  • Product dimensions: 8.70 (w) x 10.90 (h) x 1.30 (d)

Meet 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.

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Table of Contents


Course Management Notes (Instructor’s Edition only)


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

Part III: Probability, Random Variables, and Sampling Distributions

4. Probability Concepts

4.1 Probability Basics

4.2 Events

4.3 Some Rules of Probability

4.4 Contingency Tables; Joint and Marginal Probabilities*

4.5 Conditional Probability*

4.6 The Multiplication Rule; Independence*

4.7 Bayes’s Rule*

4.8 Counting Rules*

5. Discrete Random Variables*

5.1 Discrete Random Variables and Probability Distributions*

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

5.3 The Binomial Distribution*

5.4 The Poisson 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

6.5 Normal Approximation to the Binomial Distribution*

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

9.6 The Wilcoxon Signed-Rank Test*

9.7 Type II Error Probabilities; Power*

9.8 Which Procedure Should Be Used?*

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 The Mann–Whitney Test*

10.5 Inferences for Two Population Means, Using Paired Samples

10.6 The Paired Wilcoxon Signed-Rank Test*

10.7 Which Procedure Should Be Used?*

11. Inferences for Population Standard Deviations*

11.1 Inferences for One Population Standard Deviation*

11.2 Inferences for Two Population Standard Deviations, Using Independent Samples*

12. Inferences for Population Proportions

12.1 Confidence Intervals for One Population Proportion

12.2 Hypothesis Tests for One Population Proportion

12.3 Inferences for Two Population Proportions

13. Chi-Square Procedures

13.1 The Chi-Square Distribution

13.2 Chi-Square Goodness-of-Fit Test

13.3 Contingency Tables; Association

13.4 Chi-Square Independence Test

13.5 Chi-Square Homogeneity Test

Part V: Regression, Correlation, and ANOVA

14. Descriptive Methods in Regression and Correlation

14.1 Linear Equations with One Independent Variable

14.2 The Regression Equation

14.3 The Coefficient of Determination

14.4 Linear Correlation

15. Inferential Methods in Regression and Correlation

15.1 The Regression Model; Analysis of Residuals

15.2 Inferences for the Slope of the Population Regression Line

15.3 Estimation and Prediction

15.4 Inferences in Correlation

15.5 Testing for Normality*

16. Analysis of Variance (ANOVA)

16.1 The F-Distribution

16.2 One-Way ANOVA: The Logic

16.3 One-Way ANOVA: The Procedure

16.4 Multiple Comparisons*

16.5 The Kruskal–Wallis Test*

Part VI: Multiple Regression and Model Building; Experimental Design and ANOVA (On The WeissStats CD-ROM)

Module A. Multiple Regression Analysis

A.1 The Multiple Linear Regression Model

A.2 Estimation of the Regression Parameters

A.3 Inferences Concerning the Utility of the Regression Model

A.4 Inferences Concerning the Utility of Particular Predictor Variables

A.5 Confidence Intervals for Mean Response; Prediction Intervals for Response

A.6 Checking Model Assumptions and Residual Analysis

Module B. Model Building in Regression

B.1 Transformations to Remedy Model Violations

B.2 Polynomial Regression Model

B.3 Qualitative Predictor Variables

B.4 Multicollinearity

B.5 Model Selection: Stepwise Regression

B.6 Model Selection: All Subsets Regression

B.7 Pitfalls and Warnings

Module C. Design of Experiments and Analysis of Variance

C.1 Factorial Designs

C.2 Two-Way ANOVA: The Logic

C.3 Two-Way ANOVA: The Procedure

C.4 Two-Way ANOVA: Multiple Comparisons

C.5 Randomized Block Designs

C.6 Randomized Block ANOVA: The Logic

C.7 Randomized Block ANOVA: The Procedure

C.8 Randomized Block ANOVA: Multiple Comparisons

C.9 Friedman’s Nonparametric Test for the Randomized Block Design*


Appendix A: Statistical Tables

I. Random numbers

II. Areas under the standard normal curve

III. Normal scores

IV. Values of tα

V. Values of Wα

VI. Values of Mα

VII. Values of χα 2

VIII. Values of Fα

IX. Critical values for a correlation test for normality

X. Values of q 0.01

XI. Values of q 0.05

XII. Binomial probabilities

Appendix B Answers to Selected Exercises


Photo Credits

Indexes for Biographical Sketches & Case Studies

WeissStats CD-ROM (included with every new textbook)

Brief Contents

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


Data Sets

DDXL (Excel Add-In)

Detailed t and Chi-square Tables

Focus Database

Formulas and Appendix A Tables

JMP Concept Discovery Modules

Minitab Macros

Regression-ANOVA Modules

Technology Basics

TI Programs

*indicates an optional section

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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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