Classic, yet contemporary. Theoretical, yet applied. Statistics for Business and Economics, Eleventh Edition, gives you the best of both worlds. Using a rich array of applications from a variety of industries, McClave/Sincich/Benson clearly demonstrates how to use statistics effectively in a business environment. The book focuses on developing statistical thinking so the reader can better assess the credibility and value of inferences made from data. As consumers and future producers of statistical inferences, readers are introduced to a wide variety of data collection and analysis techniques to help them evaluate data and make informed business decisions. As with previous editions, this revision offers an abundance of applications with many new and updated exercises that draw on real business situations and recent economic events. The authors assume a background of basic algebra.

Table of Contents

1. Statistics, Data, and Statistical Thinking 1.1 The Science of Statistics 1.2 Types of Statistical Applications in Business 1.3 Fundamental Elements of Statistics 1.4 Processes* 1.5 Types of Data 1.6 Collecting Data 1.7 The Role of Statistics in Managerial Decision-Making 2. Methods for Describing Sets of Data 2.1 Describing Qualitative Data 2.2 Graphical Methods for Describing Quantitative Data 2.3 Summation Notation 2.4 Numerical Measures of Central Tendency 2.5 Numerical Measures of Variability 2.6 Interpreting the Standard Deviation 2.7 Numerical Measures of Relative Standing 2.8 Methods for Detecting Outliers: Box Plots and z-Scores 2.9 Graphing Bivariate Relationships* 2.10 The Time Series Plot 2.11 Distorting the Truth with Descriptive Techniques 3. Probability 3.1 Events, Sample Spaces, and Probability 3.2 Unions and Intersections 3.3 Complementary Events 3.4 The Additive Rule and Mutually Exclusive Events 3.5 Conditional Probability 3.6 The Multiplicative Rule and Independent Events 3.7 Random Sampling 3.8 Bayes' Rule 4. Random Variables and Probability Distributions 4.1 Two Types of Random Variables PART I: Discrete Random Variables 4.2 Probability Distributions for Discrete Random Variables 4.3 The Binomial Random Variable 4.4 Other Discrete Distributions: Poisson and Hypergeometric PART II: Continuous Random Variables 4.5 Probability Distributions for Continuous Random Variables 4.6 The Normal Distribution 4.7 Descriptive Methods for Assessing Normality 4.8 Approximating a Binomial Distribution with a Normal Distribution 4.9 Other Continuous Distributions: Uniform and Exponential 4.10 Sampling Distributions 4.11 The Sampling Distribution of a Sample Mean and the Central Limit Theorem 5. Inferences Based on a Single Sample: Estimation with Confidence Intervals 5.1 Identifying the Target Parameter 5.2 Confidence Interval for a Population Mean: Normal (z) Statistic 5.3 Confidence Interval for a Population Mean: Student's t-Statistic 5.4 Large-Sample Confidence Interval for a Population Proportion 5.5 Determining the Sample Size 5.6 Finite Population Correction for Simple Random Sampling 5.7 Sample Survey Designs* 6. Inferences Based on a Single Sample: Tests of Hypothesis 6.1 The Elements of a Test of Hypothesis 6.2 Formulating Hypotheses and Setting Up the Rejection Region 6.3 Test of Hypothesis about a Population Mean: Normal (z) Statistic 6.4 Observed Significance Levels: p-Values 6.4 Test of Hypothesis About a Population Mean: Student's t-Statistic 6.5 Large-Sample Test of Hypothesis About a Population Proportion 6.6 Calculating Type II Error Probabilities: More About ss* 6.7 Test of Hypothesis About a Population Variance 7. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses 7.1 Identifying the Target Parameter 7.2 Comparing Two Population Means: Independent Sampling 7.3 Comparing Two Population Means: Paired Difference Experiments 7.4 Comparing Two Population Proportions: Independent Sampling 7.5 Determining the Sample Size 7.6 Comparing Two Population Variances: Independent Sampling 8. Design of Experiments and Analysis of Variance 8.1 Elements of a Designed Experiment 8.2 The Completely Randomized Design: Single Factor 8.3 Multiple Comparisons of Means 8.4 The Randomized Block Design 8.5 Factorial Experiments 9. Categorical Data Analysis 9.1 Categorical Data and the Multinomial Experiment 9.2 Testing Category Probabilities: One-Way Table 9.3 Testing Category Probabilities: Two-Way (Contingency) Table 9.4 A Word of Caution About Chi-Square Tests 10. Simple Linear Regression 10.1 Probabilistic Models 10.2 Fitting the Model: The Least Squares Approach 10.3 Model Assumptions 10.4 Assessing the Utility of the Model: Making Inferences about the Slope ss1 10.5 The Coefficients of Correlation and Determination 10.6 Using the Model for Estimation and Prediction 10.7 A Complete Example 11. Multiple Regression and Model Building 11.1 Multiple Regression Models PART I: First-Order Models with Quantitative Independent Variables 11.2 The First-Order Model: Estimating and Making Inferences about the ss-Parameters 11.3 Evaluating Overall Model Utility 11.4 Using the Model for Estimation and Prediction PART II: Model Building in Multiple Regression 11.5 Model Building: Interaction Models 11.6 Model Building: Quadratic and other Higher-Order Models 11.7 Model Building: Qualitative (Dummy) Variable Models 11.8 Model Building: Models with both Quantitative and Qualitative Variables 11.9 Model Building: Comparing Nested Models 11.10 Model Building: Stepwise Regression PART III: Multiple Regression Diagnostics 11.11 Residual Analysis: Checking the Regression Assumptions 11.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation 12. Methods for Quality Improvement: Statistical Process Control 12.1 Quality, Processes, and Systems 12.2 Statistical Control 12.3 The Logic of Control Charts 12.4 A Control Chart for Monitoring the Mean of a Process: The x-Chart 12.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart 12.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart 12.7 Diagnosing the Causes of Variation 12.8 Capability Analysis 13. Time Series: Descriptive Analyses, Models, and Forecasting (Available on CD) 13.1 Descriptive Analysis: Index Numbers 13.2 Descriptive Analysis: Exponential Smoothing 13.3 Time Series Components 13.4 Forecasting: Exponential Smoothing 13.5 Forecasting Trends: The Holt's Method 13.6 Measuring Forecast Accuracy: MAD and RMSE 13.7 Forecasting Trends: Simple Linear Regression 13.8 Seasonal Regression Models 13.9 Autocorrelation and the Durbin-Watson Test 14. Nonparametric Statistics (available on CD) 14.1 Introduction: Distribution-Free Tests 14.2 Single Population Inferences 14.3 Comparing Two Populations: Independent Samples 14.4 Comparing Two Populations: Paired Difference Experiment 14.5 Comparing Three or More Populations: Completely Randomized Design 14.6 Comparing Three or More Populations: Randomized Block Design 14.7 Rank Correlation * Optional Topic Appendix A Basic Counting Rules Appendix B Tables Appendix C Calculation Formulas for Analysis of Variance

About the Author

Dr. Jim McClave is currently President and CEO of Info Tech, Inc., a statistical consulting and software development firm with an international clientele. He is an Adjunct Professor of Statistics at the University of Florida, where he was a full-time member of the faculty for 20 years. P. George Benson is the 21st president of the College of Charleston. Prior to his appointment, he was Dean at the University of Georgia's C. Herman and Mary Virginia Terry College of Business. His research interests include quality management, strategic management, belief formation, and judgmental forecasting. He consults nationally in the areas of applied statistics, quality management, and employment discrimination. Terry Sincich obtained his PhD in statistics from the University of Florida in 1980. He is an Associate Professor in the Information Systems & Decision Sciences Department at the University of South Florida in Tampa. Dr. Sincich is responsible for teaching basic statistics to all undergraduates in the College of Business, as well as advanced statistics to all business doctoral candidates. He has published articles in such journals as the Journal of the American Statistical Association, International Journal of Forecasting, Academy of Management Journal, and the Auditing: A Journal of Practice & Theory. Dr. Sincich is a co-author of the texts Statistics, A First Course in Statistics, Statistics for Engineering & the Sciences, and A Second Course in Statistics: Regression Analysis.