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.
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 β*
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 β_{1}
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 β-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 (**Chapter is available in PDF format on CD bound with text)
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 (**Chapter is available in PDF format on CD-ROM bound with text)
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