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Introductory Statistics / Edition 9 available in Hardcover, Paperback
Overview
Weiss’s Introductory Statistics, Ninth Edition is the ideal textbook for introductory statistics classes that emphasize statistical reasoning and critical thinking. The text is suitable for a one or twosemester course. Comprehensive in its coverage, Weiss’s meticulous style offers careful, detailed explanations to ease the learning process. With more than 1,000 data sets and more than 2,600 exercises, most using real data, this text takes a datadriven approach that encourages students to apply their knowledge and develop statistical literacy.
Introductory Statistics, Ninth Edition, contains parallel presentation of criticalvalue and pvalue 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.
This edition continues the book’s tradition of being on the cutting edge of statistical pedagogy, technology, and data analysis. It includes hundreds of new and updated exercises with real data from journals, magazines, newspapers, and websites.
Datasets and other resources (where applicable) for this book are available here.
Product Details
ISBN13:  9780321691224 

Publisher:  Pearson 
Publication date:  01/10/2011 
Edition description:  Older Edition 
Pages:  896 
Product dimensions:  8.70(w) x 10.90(h) x 1.30(d) 
Read an Excerpt
Weiss’s Introductory Statistics, Ninth Edition is the ideal textbook for introductory statistics classes that emphasize statistical reasoning and critical thinking. The text is suitable for a one or twosemester course. Comprehensive in its coverage, Weiss’s meticulous style offers careful, detailed explanations to ease the learning process. With more than 1,000 data sets and more than 2,600 exercises, most using real data, this text takes a datadriven approach that encourages students to apply their knowledge and develop statistical literacy.
Introductory Statistics, Ninth Edition, contains parallel presentation of criticalvalue and pvalue 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.
This edition continues the book’s tradition of being on the cutting edge of statistical pedagogy, technology, and data analysis. It includes hundreds of new and updated exercises with real data from journals, magazines, newspapers, and websites.
Datasets and other resources (where applicable) for this book are available here.
First Chapter
Weiss’s Introductory Statistics, Ninth Edition is the ideal textbook for introductory statistics classes that emphasize statistical reasoning and critical thinking. The text is suitable for a one or twosemester course. Comprehensive in its coverage, Weiss’s meticulous style offers careful, detailed explanations to ease the learning process. With more than 1,000 data sets and more than 2,600 exercises, most using real data, this text takes a datadriven approach that encourages students to apply their knowledge and develop statistical literacy.
Introductory Statistics, Ninth Edition, contains parallel presentation of criticalvalue and pvalue 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.
This edition continues the book’s tradition of being on the cutting edge of statistical pedagogy, technology, and data analysis. It includes hundreds of new and updated exercises with real data from journals, magazines, newspapers, and websites.
Datasets and other resources (where applicable) for this book are available here.
Table of Contents
Preface
Course Management Notes (Instructor’s Edition only)
Supplements
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 FiveNumber 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 CriticalValue Approach to Hypothesis Testing
9.3 PValue 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 SignedRank 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 SignedRank 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. ChiSquare Procedures
13.1 The ChiSquare Distribution
13.2 ChiSquare GoodnessofFit Test
13.3 Contingency Tables; Association
13.4 ChiSquare Independence Test
13.5 ChiSquare 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 FDistribution
16.2 OneWay ANOVA: The Logic
16.3 OneWay 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 CDROM)
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 TwoWay ANOVA: The Logic
C.3 TwoWay ANOVA: The Procedure
C.4 TwoWay 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*
APPENDICES
Appendix A: Statistical Tables
I. Random numbers
II. Areas under the standard normal curve
III. Normal scores
IV. Values of t_{α}
V. Values of W_{α}
VI. Values of M_{α}
VII. Values of χ_{α} ^{2}
VIII. Values of F_{α}
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
Index
Photo Credits
Indexes for Biographical Sketches & Case Studies
WeissStats CDROM (included with every new textbook)
Brief Contents
Note: See the WeissStats CD ReadMe file for detailed contents.
Applets
Data Sets
DDXL (Excel AddIn)
Detailed t and Chisquare Tables
Focus Database
Formulas and Appendix A Tables
JMP Concept Discovery Modules
Minitab Macros
RegressionANOVA Modules
Technology Basics
TI Programs
*indicates an optional section
Reading Group Guide
Preface
Course Management Notes (Instructor’s Edition only)
Supplements
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 FiveNumber 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 CriticalValue Approach to Hypothesis Testing
9.3 PValue 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 SignedRank 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 SignedRank 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. ChiSquare Procedures
13.1 The ChiSquare Distribution
13.2 ChiSquare GoodnessofFit Test
13.3 Contingency Tables; Association
13.4 ChiSquare Independence Test
13.5 ChiSquare 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 FDistribution
16.2 OneWay ANOVA: The Logic
16.3 OneWay 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 CDROM)
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 TwoWay ANOVA: The Logic
C.3 TwoWay ANOVA: The Procedure
C.4 TwoWay 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*
APPENDICES
Appendix A: Statistical Tables
I. Random numbers
II. Areas under the standard normal curve
III. Normal scores
IV. Values of t_{α}
V. Values of W_{α}
VI. Values of M_{α}
VII. Values of χ_{α} ^{2}
VIII. Values of F_{α}
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
Index
Photo Credits
Indexes for Biographical Sketches & Case Studies
WeissStats CDROM (included with every new textbook)
Brief Contents
Note: See the WeissStats CD ReadMe file for detailed contents.
Applets
Data Sets
DDXL (Excel AddIn)
Detailed t and Chisquare Tables
Focus Database
Formulas and Appendix A Tables
JMP Concept Discovery Modules
Minitab Macros
RegressionANOVA Modules
Technology Basics
TI Programs
*indicates an optional section
Interviews
Preface
Course Management Notes (Instructor’s Edition only)
Supplements
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 FiveNumber 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 CriticalValue Approach to Hypothesis Testing
9.3 PValue 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 SignedRank 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 SignedRank 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. ChiSquare Procedures
13.1 The ChiSquare Distribution
13.2 ChiSquare GoodnessofFit Test
13.3 Contingency Tables; Association
13.4 ChiSquare Independence Test
13.5 ChiSquare 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 FDistribution
16.2 OneWay ANOVA: The Logic
16.3 OneWay 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 CDROM)
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 TwoWay ANOVA: The Logic
C.3 TwoWay ANOVA: The Procedure
C.4 TwoWay 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*
APPENDICES
Appendix A: Statistical Tables
I. Random numbers
II. Areas under the standard normal curve
III. Normal scores
IV. Values of t_{α}
V. Values of W_{α}
VI. Values of M_{α}
VII. Values of χ_{α} ^{2}
VIII. Values of F_{α}
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
Index
Photo Credits
Indexes for Biographical Sketches & Case Studies
WeissStats CDROM (included with every new textbook)
Brief Contents
Note: See the WeissStats CD ReadMe file for detailed contents.
Applets
Data Sets
DDXL (Excel AddIn)
Detailed t and Chisquare Tables
Focus Database
Formulas and Appendix A Tables
JMP Concept Discovery Modules
Minitab Macros
RegressionANOVA Modules
Technology Basics
TI Programs
*indicates an optional section
Preface
Preface
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 reallife opportunities for applying them.
About This Book
The text is intended for a one or twosemestercourse and for quartersystem 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/MAAGuidelinesCompliant. We followASA/MAA guidelines to stress the interpretation of statistical results, thecontemporary applications of statistics, and the importance of criticalthinking.
Unique VariableCentered 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 CriticalValue/PValue Approaches. Through aparallel presentation, the book offers complete flexibility in the coverage ofthe criticalvalue and Pvalueapproaches to hypothesis testingeither 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 TI83/84 Plus. One or more technologies can beexplored and compared.
New and Hallmark Features
ChapterOpening 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 realworld relevance of the material. (Case studies arereviewed and discussed at the end of the chapter.) More than onethird of thecase studies are new or updated.
RealWorld Examples.Every concept discussed in the text is illustrated by at least onedetailed example. The examples are based on reallife 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 easytofollow, stepbystep 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 intext coverage of statistical technology includes three of themost popular applications: Minitab, Excel, and the TI83/84 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,realworld 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 TI83/84 Plus,SPSS, or any other statistical technology.
EndofChapter 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.
AwardWinning 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 18887770463.
Acknowledgments
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
TzeSan 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
Recipe
Preface
Course Management Notes (Instructor’s Edition only)
Supplements
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 FiveNumber 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 CriticalValue Approach to Hypothesis Testing
9.3 PValue 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 SignedRank 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 SignedRank 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. ChiSquare Procedures
13.1 The ChiSquare Distribution
13.2 ChiSquare GoodnessofFit Test
13.3 Contingency Tables; Association
13.4 ChiSquare Independence Test
13.5 ChiSquare 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 FDistribution
16.2 OneWay ANOVA: The Logic
16.3 OneWay 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 CDROM)
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 TwoWay ANOVA: The Logic
C.3 TwoWay ANOVA: The Procedure
C.4 TwoWay 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*
APPENDICES
Appendix A: Statistical Tables
I. Random numbers
II. Areas under the standard normal curve
III. Normal scores
IV. Values of t_{α}
V. Values of W_{α}
VI. Values of M_{α}
VII. Values of χ_{α} ^{2}
VIII. Values of F_{α}
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
Index
Photo Credits
Indexes for Biographical Sketches & Case Studies
WeissStats CDROM (included with every new textbook)
Brief Contents
Note: See the WeissStats CD ReadMe file for detailed contents.
Applets
Data Sets
DDXL (Excel AddIn)
Detailed t and Chisquare Tables
Focus Database
Formulas and Appendix A Tables
JMP Concept Discovery Modules
Minitab Macros
RegressionANOVA Modules
Technology Basics
TI Programs
*indicates an optional section
Customer Reviews
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