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.

1.1 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 β₁

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

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