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.

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 t­ -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 R 2 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