Preface Acknowledgments Dear Students   1. An Introduction to Business Statistics 1.1 Business Statistics and Their Uses 1.2 Data 1.3 Descriptive and Inferential Statistics 1.4 Ethics and Statistics—It’s a Dangerous World of Data Out There   2. Displaying Descriptive Statistics 2.1 The Role Technology Plays in Statistics 2.2 Displaying Quantitative Data 2.3 Displaying Qualitative Data 2.4 Contingency Tables 2.5 Stem and Leaf Display 2.6 Scatter Plots   3. Calculating Descriptive Statistics 3.1 Measures of Central Tendency 3.2 Measures of Variability 3.3 Using the Mean and Standard Deviation Together 3.4 Working with Grouped Data 3.5 Measures of Relative Position 3.6 Measures of Association Between Two Variables   4. Introduction to Probabilities 4.1 An Introduction to Probabilities 4.2 Probability Rules for More Than One Event 4.3 Counting Principles     5. Discrete Probability Distributions 5.1 Introduction to Discrete Probability Distributions 5.2 Binomial Distributions 5.3 Poisson Distributions 5.4 The Hypergeometric Distribution   6. Continuous Probability Distributions 6.1 Continuous Random Variables 6.2 Normal Probability Distributions 6.3 Exponential Probability Distributions 6.4 Uniform Probability Distributions   7. Sampling and Sampling Distributions 7.1 Why Sample? 7.2 Types of Sampling 7.3 Sampling and Nonsampling Errors 7.4 The Central Limit Theorem 7.5 The Sampling Distribution of the Proportion   8. Confidence Intervals 8.1 Point Estimates 8.2 Calculating Confidence Intervals for the Mean when the Standard Deviation (σ) of a Population Is Known 8.3 Calculating Confidence Intervals for the Mean when the Standard Deviation (σ) of a Population Is Unknown 8.4 Calculating Confidence Intervals for Proportions 8.5 Determining the Sample Size 8.6 Calculating Confidence Intervals for Finite Populations   9. Hypothesis Testing for a Single Population 9.1 An Introduction to Hypothesis Testing 9.2 Hypothesis Testing for the Population Mean When σ Is Known 9.3 Hypothesis Testing for the Population Mean when σ Is Unknown 9.4 Hypothesis Testing for the Proportion of a Population 9.5 Type II Errors   10. Hypothesis Tests Comparing Two Populations 10.1 Comparing Two Population Means with 10.2 Comparing Two Population Means with 10.3 Hypothesis Testing With Dependent Samples 10.4 Comparing Two Population Proportions with Independent Samples     11. Analysis of Variance (ANOVA) Procedures 11.1 One-Way ANOVA: Examining the Effect a Single Factor Has on the Means of Populations 11.2 Randomized Block ANOVA: Examining the Effects of a Single Factor by Blocking a Second Factor 11.3 Two-Way ANOVA: Examining the Effects Two Factors Have on the Means of Populations   12. Chi-Square Tests 12.1 Comparing Two or More Population Proportions 12.2 Determining If Observed Frequencies Follow a Known Probability Distribution 12.3 Testing the Independence of Two Variables   13. Hypothesis Tests for the Population Variance 13.1 Testing the Variance of a Single Population 13.2 Comparing the Variances of Two Populations   14. Correlation and Simple Linear Regression 14.1 Dependent and Independent Variables 14.2 Correlation Analysis 14.3 Simple Linear Regression Analysis 14.4 Using a Regression to Make a Prediction 14.5 Testing the Significance of the Slope of the Regression Equation 14.6 Assumptions for Regression Analysis 14.7 A Simple Regression Example with a Negative Correlation 14.8 Some Final (but Very Important) Thoughts   15. Multiple Regression and Model Building 15.1 Developing the Multiple Regression Model 15.2 Explaining the Variation of the Dependent Variable 15.3 Inferences about the Independent Variables 15.4 Using Qualitative Independent Variables 15.5 Model Building   16. Forecasting 16.1 Introduction to Forecasting 16.2 Smoothing Forecasting Methods 16.3 Forecasting with Regression Analysis 16.4 Forecasting with Seasonality   17. Decision Analysis 17.1 Introduction to Decision Analysis 17.2 Constructing a Decision Table 17.3 Decision Making Under Uncertainty 17.4 Decision Making Under Risk 17.5 Decision Making Using Decision Trees 17.6 Using Bayes’ Theorem to Calculate Posterior Probabilities   18. Nonparametric Statistics 18.1 Introduction to Nonparametric Statistics 18.2 The Sign Test 18.3 The Wilcoxon Rank-Sum Test for Two Independent Samples 18.4 The Wilcoxon Signed-Rank Test for Two Dependent Samples 18.5 The Kruskal-Wallis One-Way ANOVA 18.6 The Spearman Rank-Order Correlation Coefficient   Appendix A Table 1 Binomial Probabilities Table 2 Poisson Probabilities Table 3 Cumulative Probabilities for the Standard Normal Distribution Table 4 Cumulative Probabilities for the Standard Normal Distribution Table 5 Student’s t-distribution Table 6 F-distribution Table 7 Critical Values of the Studentized Range, Q Table 8 Chi–Square Distribution Table 9 Critical Values for the Durbin-Watson Statistic   Appendix B: Answers to Selected Even-Numbered Problems   Index of Applications Index  

New and Updated Features Learning Objectives open every chapter. StatCrunch™: MyStatLab includes a web-based statistical software, StatCrunch, within the online assessment platform so that students can easily analyze data sets from exercises and the text. In addition, MyStatLab includes access to www.StatCrunch.com, a website where users can access more than thirteen thousand shared data sets, conduct online surveys, perform complex analyses using the powerful statistical software, and generate compelling reports. Updates to this edition include the most current thinking and practices in the field. Updated technology coverage includes Microsoft Excel 2013, with instructions for Excel 2010 provided online as needed. Step by step instructions for Mac and Excel 2010 users are on the text’s web site: www.pearsonhighered.com/donnelly. More problems—25% more than in the previous edition, for a total of over 1,110 business-related problems, 35% of which are new or updated. Twice as many data sets—over 340—included in the problems, examples, and Your Turn problems. Excel functions are now used to determine p-values and critical scores for hypothesis tests that use the normal, student’s t, F, and chi-square distributions, giving students more options for this type of analysis. Streamlined procedures include the removal of critical sample mean and critical sample proportion as optional steps to hypothesis testing in Chapters 9 and 10. These two topics are now included in the section describing Type II Errors at the end of Chapter 9.   Content Updates Two new chapters cover Decision Analysis (Chapter 17) and Nonparametric Statistics (Chapter 18). These are available online. A new Chapter 17, Decision Analysis, provides a detailed discussion of decision making under uncertainty and decision making under risk, along with a step-by-step description on the construction and analysis of decision trees.  A new Chapter 18, Nonparametric Statistics, looks at the following procedures: Sign Test, Wilcoxon Rank-Sum Test, Wilcoxon Signed-Rank Test, Kruskal-Wallis One-Way ANOVA, and Spearman Rank-Order Correlation Coefficient. New topics of covariance and the correlation of coefficient introduced at the end of Chapter 3, Calculating Descriptive statistics. The correlation coefficient is also covered in Chapter 14, Correlation and Simple Regression. Additional features new to this edition make a good text even better: Index of Applications Redesigned end-of-chapter sections Learning Objectives added to chapter openers