Business Statistics, Second Edition, helps students gain the statistical tools and develop the understanding they’ll need to make informed business decisions using data. The dynamic approach conquers the modern challenges of teaching business statistics by making it relevant, emphasizing analysis and understanding over simple computation, preparing students to be more analytical, make better business decisions, and effectively communicating results.   This text features a wealth of real data applications, with coverage of current issues including ethics and data mining. It draws readers in using a conversational writing style and delivers content with a fresh, exciting approach that reflects the authors’ blend of teaching, consulting, and entrepreneurial experiences. Learning tools such as the Plan/Do/Report guided examples prepare students to tackle any business problem they will encounter as a future business leader.   This book follows the GAISE Guidelines, emphasizing real data and real-world interpretations of analyses.

Table of Contents:
EXPLORING AND COLLECTING DATA   1. Statistics and Variation 1.1 So, What Is Statistics? 1.2 How Will This Book Help?   2. Data Amazon.com 2.1 What Are Data? 2.2 Variable Types 2.3 Data Sources: Where, How, and When Ethics in Action Technology Help Brief Cases: Credit Card Bank   3. Surveys and Sampling Roper Polls 3.1 Three Ideas of Sampling 3.2 Populations and Parameters 3.3 Other Sample Designs 3.4 The Valid Survey 3.5 How to Sample Badly Ethics in Action Technology Help: Random Sampling Brief Cases: Market Survey Research The GfK Roper Reports Worldwide Survey   4. Displaying and Describing Categorical Data Keen 4.1 Summarizing a Categorical Variable 4.2 Displaying a Categorical Variable 4.3 Exploring Two Categorical Variables: Contingency Tables Ethics in Action Technology Help: Displaying Categorical Data on the Computer Brief Cases: KEEN   5. Displaying and Describing Quantitative Data AIG 5.1 Displaying Quantitative Variables 5.2 Shape 5.3 Center 5.4 Spread of the Distribution 5.5 Shape, Center, and Spread-A Summary 5.6 Five-Number Summary and Boxplots 5.7 Comparing Groups 5.8 Identifying Outliers 5.9 Standardizing *5.10 Time Series Plots *5.11 Transforming Skewed Data Ethics in Action Technology Help: Displaying and Summarizing Quantitative Variables Brief Cases Hotel Occupancy Rates 122 Value and Growth Stock Returns 122   6. Correlation and Linear Regression Lowe's 6.1 Looking at Scatterplots 6.2 Assigning Roles to Variables in Scatterplots 6.3 Understanding Correlation 6.4 Lurking Variables and Causation 6.5 The Linear Model 6.6 Correlation and the Line 6.7 Regression to the Mean 6.8 Checking the Model 6.9 Variation in the Model and R2 6.10 Reality Check: Is the Regression Reasonable? 6.11 Non-linear Relationships Ethics in Action Technology Help: Correlation and Regression Brief Cases: Fuel Efficiency The U.S. Economy and Home Depot Stock Prices Cost of Living Mutual Funds   Case Study: Paralyzed Veterans of America   PART II. MODELING WITH PROBABLITY   7. Randomness and Probability Credit Reports and the Fair Isaacs Corporation 7.1 Random Phenomena and Probability 7.2 The Nonexistent Law of Averages 7.3 Different Types of Probability 7.4 Probability Rules 7.5 Joint Probability and Contingency Tables 7.6 Conditional Probability 7.7 Constructing Contingency Tables Brief Case: Market Segmentation   8. Random Variables and Probability Models Metropolitan Life Insurance Company 8.1 Expected Value of a Random Variable 8.2 Standard Deviation of a Random Variable 8.3 Properties of Expected Values and Variances 8.4 Discrete Probability Distributions Ethics in Action Brief Case: Investment Options   9. The Normal Distribution The NYSE 9.1 The Standard Deviation as a Ruler 9.2 The Normal Distribution 9.3 Normal Probability Plots 9.4 The Distribution of Sums of Normals 9.5 The Normal Approximation for the Binomial 9.6 Other Continuous Random Variables Ethics In Action Brief Cases: The CAPE10 Technology Help: Making Normal Probability Plots   10. Sampling Distributions Marketing Credit Cards: The MBNA Story 10.1 The Distribution of Sample Proportions 10.2 Sampling Distribution for Proportions 10.3 The Central Limit Theorem 10.4 The Sampling Distribution of the Mean 10.5 How Sampling Distribution Models Work Ethics in Action Brief Cases Real Estate Simulation Part 1: Proportions Means   Case Study: Investigating the Central Limit Theorem   PART III. INFERENCE FOR DECISION MAKING   11. Confidence Intervals for Proportions The Gallup Organization 11.1 A Confidence Interval 11.2 Margin of Error: Certainty vs. Precision 11.3 Assumptions and Conditions 11.4 Choosing the Sample Size *11.5 A Confidence Interval for Small Samples Ethics in Action Technology Help: Confidence Intervals for Proportions Brief Cases: Investment Forecasting Demand   12. Confidence Intervals for Means Guinness & Co. 12.1 The Sampling Distribution for the Mean 12.2 A Confidence Interval for Means 12.3 Assumptions and Conditions 12.4 Cautions About Interpreting Confidence Intervals 12.5 Sample Size 12.6 Degrees of Freedom - Why (n-1)? Ethics in Action Technology Help: Inference for Means Brief Cases: Real Estate Donor Profiles   13. Testing Hypotheses Dow Jones Industrial Average 13.1 Hypotheses 13.2 A Trial as a Hypothesis Test 13.3 P-values 13.4 The Reasoning of Hypothesis Testing 13.5 Alternative Hypotheses 13.6 Testing Hypothesis about Means - the One 13.7 Alpha Levels and Significance 13.8 Critical Values 13.9 Confidence Intervals and Hypothesis Tests 13.10 Two Types of Errors *13.11 Power Ethics in Action Technology Help Brief Cases: Metal Production Loyalty Program   14. Comparing Two Groups Visa Global Organization 14.1 Comparing Two Means 14.2 The Two-Sample t-Test 14.3 Assumptions and Conditions 14.4 A Confidence Interval for the Difference Between Two Means 14.5 The Pooled t-Test 14.6 Tukey's Quick Test 14.7 Paired Data 14.8 The Paired t-Test Ethics in Action Technology Help: Two-Sample Methods Brief Cases: Real Estate Consumer Spending Patterns (Data Analysis)   15. Inference for Counts: Chi-Square Tests SAC Capital 15.1 Goodness-of-Fit Tests 15.2 Interpreting Chi-Square Values 15.3 Examining the Residuals 15.4 The Chi-Square Test of Homogeneity 15.5 Comparing Two Proportions 15.6 Chi-Square Test of Independence Ethics in Action Technology Help: Chi-Square Brief Cases: Health Insurance Loyalty Program   Case Study   Part IV. MODELS FOR DECISION MAKING   16. Inference for Regression Nambé Mills 16.1 The Population and the Sample 16.2 Assumptions and Conditions 16.3 The Standard Error of the Slope 16.4 A Test for the Regression Slope 16.5 A Hypothesis Test for Correlation 16.6 Standard Errors for Predicted Values 16.7 Using Confidence and Prediction Intervals Ethics in Action Technology Help: Regression Analysis Brief Cases: Frozen Pizza Global Warming?   17. Understanding Residuals Kellogg's 17.1 Examining Residuals for Groups 17.2 Extrapolation and Prediction 17.3 Unusual and Extraordinary Observations 17.4 Working with Summary Values 17.5 Autocorrelation 17.6 Transforming (Re-expressing) Data 17.7 The Ladder of Powers Ethics in Action Technology Help Brief Cases: Gross Domestic Product Energy Sources   18. Multiple Regression Zillow.com 18.1 The Multiple Regression Model 18.2 Interpreting Multiple Regression Coefficients 18.3 Assumptions and Conditions for the Multiple Regression Model 18.4 Testing the Multiple Regression Model 18.5 Adjusted R2, and the F-statistic *18.6 The Logistic Regression Model Ethics in Action Technology Help: Regression Analysis Brief Case: Golf Success   19. Building Multiple Regression Models Bolliger and Mabillard 19.1 Indicator (or Dummy) Variables 19.2 Adjusting for Different Slopes-Interaction 19.3 Multiple Regression Diagnostics 19.4 Building Regression Models 19.5 Collinearity 19.6 Quadratic Ethics in Action Technology Help: Regression Analysis on the Computer Brief Cases: Paralyzed Veterans of America   20. Time Series Analysis Whole Foods Market® 20.1 What is a Time-Series? 20.2 Components of a Time Series 20.3 Smoothing Methods 20.4 Summarizing Forecast Error 20.5 Autoregressive Models 20.6 Multiple Regression-based Models 20.7 Choosing a Time Series Forecasting Method 20.8 Interpreting Time Series Models: The Whole Foods Data Revisited Ethics in Action Technology Help Brief Cases: Intel Corporation Tiffany & Co. Case Study: title to come   PART V. SELECTED TOPICS IN DECISION MAKING   21. Design and Analysis of Experiments and Observational Studies Capital One 21.1 Observational Studies 21.2 Randomized, Comparative Experiments 21.3 The Four Principles of Experimental Design 21.4 Experimental Designs 21.5 Issues in Experimental Design 21.6 Analyzing a Completely Randomized Design in One Factor-The One-Way Analysis of Variance 21.7 Assumptions and Conditions for ANOVA *21.8 Multiple Comparisons 21.9 ANOVA on Observational Data 21.10 Analysis of Multi Factor Designs Ethics in Action Technology Help Brief Cases: A Multifactor Experiment   22. Quality Control Sony 22.1 A Short History of Quality Control 22.2 Control Charts for Individual Observations (Run Charts) 22.3 Control Charts for Measurements: X and R Charts 22.4 Actions for Out of Control Processes 22.5 Control Charts for Attributes: p Charts and c Charts 22.6 Philosophies of Quality Control Ethics in Action Technology Help: Quality Control Charts Brief Cases   23. Nonparametric Methods i4cp 23.1 Ranks 23.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic 23.3 Kruskal-Wallace Test 23.4 Paired Data: The Wilcoxon Signed-Rank Test *23.5 Friedman Test for a Randomized Block Design 23.6 Kendall's Tau: Measuring Monotonicity 23.7 Spearman's Rho 23.8 When Should You Use Nonparametric Methods? Ethics in Action Brief Cases: Real Estate Reconsidered     24. Decision Making and Risk Data Description, Inc. 24.1 Actions, States of Nature, and Outcomes 24.2 Payoff Tables and Decision Trees 24.3 Minimizing Loss and Maximizing Gain 24.4 The Expected Value of an Action 24.5 Expected Value with Perfect Information 24.6 Decisions Made with Sample Information 24.7 Estimating Variation 24.8 Sensitivity 24.9 Simulation 24.10 Probability Trees *24.11 Reversing the Conditioning: Bayes's Rule 24.12 More Complex Decisions Ethics in Action Brief Cases: Texaco-Pennzoil Insurance Services, Revisited   25. Introduction to Data Mining Paralyzed Veterans of America 25.1 Direct Marketing 25.2 The Data 25.3 The Goals of Data Mining 25.4 Data Mining Myths 25.5 Successful Data Mining 25.6 Data Mining Problems 25.7 Data Mining Algorithms 25.8 The Data Mining Process 25.9 Summary Ethics in Action   Case Study   *Indicates an optional topic   Appendices A. Answers B. XLStat C. Photo Acknowledgments D. Tables and Selected Formulas E. Index