1. Statistics, Data, and Statistical Thinking.
The Science of Statistics. Types of Statistical Applications in Business. Fundamental Elements of Statistics. Processes (Optional). Types of Data. Statistics in Action 1.1: Quality Improvement: U.S. Firms Respond to the Challenge from Japan. Collecting Data. The Role of Statistics in Managerial Decision-Making. Statistics in Action 1.2: A 20/20 View of Survey Results—Fact or Fiction?
2. Methods for Describing Sets of Data.
Describing Qualitative Data. Statistics in Action 2.1: Pareto Analysis. Graphical Methods for Describing Quantitative Data. Summation Notation. Numerical Measures of Central Tendency. Numerical Measures of Variability. Interpreting the Standard Deviation. Numerical Measures of Relative Standing. Methods for Detecting Outliers (Optional). Graphing Bivariate Relationships. (Optional). The Time Series Plot (Optional). Distorting the Truth with Descriptive Techniques. Statistics in Action 2.2: Car and Driver's “Road Test Digest.” Real World Case: The Kentucky Milk Case, Part I. (A Case Covering Chapters 1 and 2.
Events, Sample Spaces, and Probability. Statistics in Action 3.1: Game Show Strategy: To Switch or Not to Switch? Unions and Intersections. Complementary Events. The Additive Rule and Mutually Exclusive Events. Conditional Probability. The Multiplicative Rule and Independent Events. Random Sampling. Statistics in Action 3.2: Lottery Buster.
4. Discrete RandomVariables.
Two Types of Random Variables. Probability Distributions for Discrete Random Variables. Expected Values of Discrete Random Variables. Statistics in Action 4.1: Portfolio Selection. The Binomial Random Variable. Statistics in Action 4.2: The Space Shuttle Challenger: Catastrophe in Space. The Poisson Random Variable (Optional).
5. Continuous Random Variables.
Continuous Probability Distributions. The Uniform Distribution (Optional). The Normal Distribution. Statistics in Action 5.1: IQ, Economic Mobility, and the Bell Curve. Descriptive Methods for Assessing Normality. Approximating a Binomial Distribution with a Normal Distribution. The Exponential Distribution (Optional). Statistics in Action 5.2: Queueing Theory.
6. Sampling Distributions.
The Concept of Sampling Distributions. Properties of Sampling Distributions: Unbiasedness and Minimum Variance (Optional). Statistics in Action 6.1: Reducing Investment Risk through Diversification. The Central Limit Theorem. Statistics in Action 6.2: The Insomnia Pill. Real World Case: The Furniture Fire Case (A Case Covering Chapters 3-6).
7. Inferences Based on a Single Sample: Estimation with Confidence Intervals.
Large-Sample Confidence Interval for a Population Mean. Small-Sample Confidence Interval for a Population Mean. Statistics in Action 7.1: Scallops, Sampling, and the Law. Large-Sample Confidence Interval for a Population Proportion. Determining the Sample Size. Finite Population Correction for Simple Random Sampling (Optional). Sample Survey Designs (Optional). Statistics in Action 7.2: Sampling Error Versus Nonsampling Error.
8. Inferences Based on a Single Sample: Tests of Hypothesis.
The Elements of a Test of Hypothesis. Large-Sample Test of Hypothesis about a Population Mean. Statistics in Action 8.1: Statistical Quality Control. Observed Significance Levels: p-Values. Small-Sample Test of Hypothesis about a Population Mean. Large-Sample Test of Hypothesis about a Population Proportion. Calculation Type II Error Probabilities: More about
b (Optional). Test of Hypothesis about a Population Variance (Optional). Statistics in Action 8.2: March Madness—Handicapping the NCAA Basketball Tourney.
9. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses.
Comparing Two Population Means: Independents Sampling. Statistics in Action 9.1: The Effect of Self-Managed Work Teams on Family Life. Comparing Two Population Means: Paired Difference Experiments. Comparing Two Population Proportions: Independent Sampling. Determining the Sample Size. Statistics in Action 9.2: Unpaid Overtime and the Fair Labor Standards Act. Comparing Two Population Variances: Independent Sampling (Optional). Real World Case: The Kentucky Milk Case, Part II (A Case Covering Chapters 7-9).
10. Simple Linear Regression.
Probabilistic Models. Fitting the Model: The Least Squares Approach. Model Assumptions. An Estimator of
s2. Assessing the Utility of the Model: Making Inferences about the Slope
b1. The Coefficient of Correlation. The Coefficient of Determination. Using the Model for Estimation and Prediction. Statistics in Action 10.1: Statistical Assessment of Damage to Bronx Bricks. Simple Linear Regression: A Complete Example. Statistics in Acton 10.2: Can “Dowsers” Really Detect Water?
11. Multiple Regression and Model Building.
Multiple Regression Models. The First-Order Model: Estimation and Interpreting the
b-Parameters. Model Assumptions. Inferences about the
b Parameters. Checking the Overall Utility of a Model. Statistics in Action 11.1: Prediction the Price of Vintage Red Bordeaux Wine. Using the Model for Estimation and Prediction. Using the Model for Estimation and Prediction. Model Building: Interaction Models. Model Building: Quadratic and Other Higher-Order Models. Model Building: Qualitative (Dummy) Variable Models. Model Building: Models with Both Quantitative and Qualitative Variables (Optional). Model Building: Comparing Nested Models (Optional). Model Building: Stepwise Regression (Optional). Residual Analysis: Checking the Regression Assumptions. Some Pitfalls: Estimability, Multicollinearity, and Extrapolation. Statistics in Action 11.2: “Wringing” The Bell Curve.
12. Methods for Quality Improvement.
Quality, Processes, and Systems. Statistics in Action 12. 1: Deming's 14 Points. Statistical Control. The Logic of Control Charts. A Control Chart for Monitoring the Mean of a Process: The x-Chart. A Control Chart for Monitoring the Variation of a Process: The R-Chart. A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart. Diagnosing the Causes of Variation (Optional). Statistics in Action 12.2: Quality Control in a Service Operation. Capability Analysis (Optional).
13. Time Series: Descriptive Analyses, Models, and Forecasting.
Descriptive Analysis: Index Numbers. Statistics in Action 13.1: The Consumer Price Index: CPI-U and CPI-W. Descriptive Analysis: Exponential Smoothing. Time Series Components. Forecasting: Exponential Smoothing. Forecasting Trends: The Holt-Winters Forecasting Model (Optional). Measuring Forecast Accuracy: MAD and RMSE. Forecasting Trends: Simple Linear Regression. Seasonal Regression Models. Statistics in Action 13.2: Forecasting the Demand for Emergency Room Services. Autocorrelation and the Durbin-Watson Test. Real World Case: The Gasket Manufacturing Case (A Case Covering Chapters 12 and 13).
14. Design of Experiments and Analysis of Variance.
Elements of a Designed Experiment. The Completely Randomized Design: Single Factor. Multiple Comparisons of Means. Statistics in Action 14.1: Is Therapy the New Diet Pill for Binge Eaters? Factorial Experiments. Statistics in Action 14.2: On the Trail of the Cockroach. Using Regression Analysis for ANOVA (Optional).
15. Nonparametric Statistics.
Introduction: Distribution-Free Tests. Single Population Inferences: The Sign Test. Comparing Two Populations: The Wilcoxon Rank Sum Test for Independent Samples. Comparing Two Populations: The Wilcoxon Signed Rank Test for the Paired Difference Experiment. Statistics in Action 15.1: Reanalyzing the Scallop Weight Data. The Kruskal-Wallis H-Test for a Completely Randomized Design. Statistics in Action 15.2: Taxpayers Versus the IRS: Selecting the Trial Court. Spearman's Rank Correlation Coefficient.
16. Categorical Data Analysis.
Categorical Data and the Multinomial Experiment. Testing Category Probabilities: One-Way Table. Testing Category Probabilities: Two-Way (Contingency) Table. Statistics in Action 16.1: Ethics in Computer Technology and Use. A Word of Caution about Chi-Square Tests. Real World Case: Discrimination in the Workplace (A Case Covering Chapters 14-16).
Basic Counting Rules. Tables. Calculation Formulas for Analysis of Variance.