It includes appendixes and index pages

Business Statistics, Third Edition, by Sharpe, De Veaux, and Velleman, narrows the gap between theory and practice—relevant statistical methods empower business students to make effective, data-informed decisions. With their unique blend of teaching, consulting, and entrepreneurial experiences, this dynamic author team brings a modern edge to teaching statistics to business students. Focusing on statistics in the context of real business issues, with an emphasis on analysis and understanding over computation, the text helps students be analytical, prepares them to make better business decisions, and shows them how to effectively communicate results.

Table of Contents:

Preface

Index of Applications

1. Data and Decisions (E-Commerce)

1.1 Data and Decisions

1.2 Variable Types

1.3 Data Sources: Where, How, and When

Ethics in Action

Technology Help: Data on the Computer

Brief Case: Credit Card Bank

2. Displaying and Describing Categorical Data (Keen, Inc.)

2.1 Summarizing a Categorical Variable

2.2 Displaying a Categorical Variable

2.3 Exploring Two Categorical Variables: Contingency Tables

2.4 Segmented Bar Charts and Mosaic Plots

2.5 Simpson's Paradox

Ethics in Action

Technology Help: Displaying Categorical Data on the Computer

Brief Case: Credit Card Bank

3. Displaying and Describing Quantitative Data (AIG)

3.1 Displaying Quantitative Variables

3.2 Shape

3.3 Center

3.4 Spread of the Distribution

3.5 Shape, Center, and Spread–A Summary

3.6 Standardizing Variables

3.7 Five-Number Summary and Boxplots

3.8 Comparing Groups,

3.9 Identifying Outliers,

3.10 Time Series Plots

3.11 Transforming Skewed Data

Ethics in Action

Technology Help: Displaying and Summarizing Quantitative Variables

Brief Cases: Detecting the Housing Bubble and Socio-Economic Data on States

4. Correlation and Linear Regression (Amazon.com)

4.1 Looking at Scatterplots

4.2 Assigning Roles to Variables in Scatterplots

4.3 Understanding Correlation

4.4 Lurking Variables and Causation

4.5 The Linear Model

4.6 Correlation and the Line

4.7 Regression to the Mean

4.8 Checking the Model

4.9 Variation in the Model and R2

4.10 Reality Check: Is the Regression Reasonable?

4.11 Nonlinear Relationships

Ethics in Action

Technology Help: Correlation and Regression

Brief Cases: Fuel Efficiency, Cost of Living, and Mutual Funds

Case Study I: Paralyzed Veterans of America

5. Randomness and Probability (Credit Reports and the Fair Isaacs Corporation)

5.1 Random Phenomena and Probability

5.2 The Nonexistent Law of Averages

5.3 Different Types of Probability

5.4 Probability Rules

5.5 Joint Probability and Contingency Tables

5.6 Conditional Probability

5.7 Constructing Contingency Tables

5.8 Probability Trees

5.9 Reversing the Conditioning: Bayes’ Rule

Ethics in Action

Technology Help: Generating Random Numbers

Brief Case

6. Random Variables and Probability Models (Metropolitan Life Insurance Company)

6.1 Expected Value of a Random Variable

6.2 Standard Deviation of a Random Variable

6.3 Properties of Expected Values and Variances

6.4 Bernoulli Trials

6.5 Discrete Probability Models

Ethics in Action

Technology Help: Random Variables and Probability Models

Brief Case: Investment Options

7. The Normal and other Continuous Distributions (The NYSE)

7.1 The Standard Deviation as a Ruler

7.2 The Normal Distribution

7.3 Normal Probability Plots

7.4 The Distribution of Sums of Normals

7.5 The Normal Approximation for the Binomial

7.6 The Other Continuous Random Variables

Ethics in Action

Technology Help: Probability Calculations and Plots

Brief Case

8. Surveys and Sampling (Roper Polls)

8.1 Three Ideas of Sampling

8.2 Populations and Parameters

8.3 Common Sampling Designs

8.4 The Valid Survey

8.5 How to Sample Badly

Ethics in Action

Technology Help: Random Sampling

Brief Cases: Market Survey Research and The GfK Roper Reports Worldwide Survey

9. Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story)

9.1 The Distribution of Sample Proportions

9.2 A Confidence Interval

9.3 Margin of Error: Certainty vs. Precision

9.4 Choosing and Sample Size

Ethics in Action

Technology Help: Confidence Intervals for Proportions

Brief Case: Real Estate Simulation

Case Study II

10. Testing Hypotheses about Proportions (Dow Jones Industrial Average)

10.1 Hypotheses

10.2 A Trial as a Hypothesis Test

10.3 P-Values

10.4 The Reasoning of Hypothesis Testing

10.5 Alternative Hypotheses

10.6 p-Values and Decisions: What to Tell About a Hypothesis Test

Ethics in Action

Technology Help: Hypothesis Tests

Brief Cases: Metal Production and Loyalty Program

11. Confidence Intervals and Hypothesis Tests for Means (Guinness & Co.)

11.1 The Central Limit Theorem

11.2 The Sampling Distribution of the Mean

11.3 How Sampling Distribution Models Work

11.4 Gossett and the tͼ/i>-Distribution

11.5 A Confidence Interval for Means

11.6 Assumptions and Conditions

11.7 Testing Hypothesis about Means–the One-Sample t-Test

Ethics in Action

Technology Help: Inference for Means

Brief Cases: Real Estate and Donor Profiles

12. More About Tests and Intervals (Traveler’s Insurance)

12.1 How to Think About P-Values

12.2 Alpha Levels and Significance

12.3 Critical Values

12.4 Confidence Intervals and Hypothesis Tests

12.5 Two Types of Errors

12.6 Power

Ethics in Action

Technology Help: Hypothesis Tests

Brief Case

13. Comparing Two Means (Visa Global Organization)

13.1 Comparing Two Means

13.2 The Two-Sample t-Test

13.3 Assumptions and Conditions

13.4 A Confidence Interval for the Difference Between Two Means

13.5 The Pooled t-Test

13.6 Paired Data

13.7 Paired Methods

Ethics in Action

Technology Help: Two-Sample Methods

Technology Help: Paired t

Brief Cases: Real Estate and Consumer Spending Patterns (Data Analysis)

14. Inference for Counts: Chi-Square Tests (SAC Capital)

14.1 Goodness-of-Fit Tests

14.2 Interpreting Chi-Square Values

14.3 Examining the Residuals

14.4 The Chi-Square Test of Homogeneity

14.5 Comparing Two Proportions

14.6 Chi-Square Test of Independence

Ethics in Action

Technology Help: Chi-Square

Brief Cases: Health Insurance and Loyalty Program



Case Study III: Investment Strategy Segmentation

15. Inference for Regression (Nambé Mills)

15.1 A Hypothesis Test and Confidence Interval for the Slope

15.2 Assumptions and Conditions

15.3 Standard Errors for Predicted Values

15.4 Using Confidence and Prediction Intervals

Ethics in Action

Technology Help: Regression Analysis

Brief Cases: Frozen Pizza and Global Warming?

16. Understanding Residuals (Kellogg’s)

16.1 Examining Residuals for Groups

16.2 Extrapolation and Prediction

16.3 Unusual and Extraordinary Observations

16.4 Working with Summary Values

16.5 Autocorrelation

16.6 Transforming (Re-expressing) Data

16.7 The Ladder of Powers

Ethics in Action

Technology Help: Examining Residuals

Brief Cases: Gross Domestic Product and Energy Sources

17. Multiple Regression (Zillow.com)

17.1 The Multiple Regression Model

17.2 Interpreting Multiple Regression Coefficients

17.3 Assumptions and Conditions for the Multiple Regression Model

17.4 Testing the Multiple Regression Model

17.5 Adjusted R2 and the F-statistic

17.6 The Logistic Regression Model

Ethics in Action

Technology Help: Regression Analysis

Brief Case: Golf Success

18. Building Multiple Regression Models (Bolliger and Mabillard)

18.1 Indicator (or Dummy) Variables

18.2 Adjusting for Different Slopes–Interaction Terms

18.3 Multiple Regression Diagnostics

18.4 Building Regression Models

18.5 Collinearity

18.6 Quadratic Terms

Ethics in Action

Technology Help: Building Multiple Regression Models

Brief Case

19. Time Series Analysis (Whole Food Market)

19.1 What Is a Time Series?

19.2 Components of a Time Series

19.3 Smoothing Methods

19.4 Summarizing Forecast Error

19.5 Autoregressive Models

19.6 Multiples Regression-based Models

19.7 Choosing a Time Series Forecasting Method

19.8 Interpreting Time Series Models: The Whole Foods Data Revisited

Ethics in Action

Technology Help

Brief Cases: Intel Corporation and Tiffany & Co.

Case Study IV: Health Care Costs

20. Design and Analysis of Experiments and Observational Studies (Capital One)

20.1 Observational Studies

20.2 Randomized Comparative Experiments

20.3 The Four Principles of Experimental Design

20.4 Experimental Designs

20.5 Issues in Experimental Design

20.6 Analyzing a Design in One Factor–The One-Way Analysis of Variance

20.7 Assumptions and Conditions for ANOVA

20.8 Multiple Comparisons

20.9 ANOVA on Observational Data

20.10 Analysis of Multifactor Designs

Ethics in Action

Technology Help: Analysis of Variance

Brief Case: Multifactor Experiment Design

21. Quality Control (Sony)

21.1 A Short History of Quality Control

21.2 Control Charts for Individual Observations (Run Charts)

21.3 Control Charts for Measurements: (x-bar) and R Charts

21.4 Actions for Out-of-Control Processes

21.5 Control Charts for Attributes: p Charts and c Charts

21.6 Philosophies of Quality Control

Ethics in Action

Technology Help: Quality Control Charts

Brief Case: Laptop Touchpad Quality

22. Nonparametric Methods (i4cp)

22.1 Ranks

22.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic

22.3 Kruskal-Wallace Test

22.4 Paired Data: The Wilcoxon Signed-Rank Test

22.5 Friedman Test for a Randomized Block Design

22.6 Kendall’s Tau: Measuring Monotonicity

22.7 Spearman’s Rho

22.8 When Should You Use Nonparametric Methods?

Ethics in Action

Technology Help

Brief Case: Real Estate Reconsidered

23. Decision Making and Risk (Data Description, Inc.)

23.1 Actions, States of Nature, and Outcomes

23.2 Payoff Tables and Decisions Trees

23.3 Minimizing Loss and Maximizing Gain

23.4 The Expected Value of an Action

23.5 Expected Value with Perfect Information

23.6 Decisions Made with Sample Information

23.7 Estimating Variation

23.8 Sensitivity

23.9 Simulation

23.10 More Complex Decisions

Ethics in Action

Technology Help

Brief Cases: Texaco-Pennzoil and Insurance Services, Revisited

24. Introduction to Data Mining (Paralyzed Veterans of America)

24.1 The Big Data Revolution

24.2 Direct Marketing

24.3 The Goals of Data Mining

24.4 Data Mining Myths

24.5 Successful Data Mining

24.6 Data Mining Problems

24.7 Data Mining Algorithms

24.8 The Data Mining Process

24.9 Summary

Ethics in Action

Case Study V Marketing Experiment

Appendices

A. Answers

B. Photo Acknowledgments

C. Tables and Selected Formulas

Index