Introductory Statistics / Edition 9

Introductory Statistics / Edition 9

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by Neil A. Weiss
     
 

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 two-semester course. Comprehensive in its coverage, Weiss’s meticulous style offers careful, detailed explanations to ease the learning process.

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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 two-semester 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 data-driven approach that encourages students to apply their knowledge and develop statistical literacy.

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.

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.

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Product Details

ISBN-13:
9780321691224
Publisher:
Pearson
Publication date:
01/10/2011
Edition description:
Older Edition
Pages:
896
Sales rank:
147,824
Product dimensions:
8.70(w) x 10.90(h) x 1.30(d)

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 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*

APPENDICES

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

Index

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.

Applets

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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