1. Introduction and Data Collection.
Why a Manager Needs to Know About Statistics. The Growth and Development of Modern Statistics. Statistical Thinking and Modern Management. Descriptive Versus Inferential Statistics. Why Do We Need Data? Types of Data. Types of Sampling Methods. Evaluating Survey Worthiness. Summary.
Appendix 1.1 Basics of the Windows User Interface. Appendix 1.2 Introduction to Microsoft Excel. Appendix 1.3 Introduction to Minitab. Case Study: Alumni Association Survey.
2. Presenting Data in Tables and Charts.
Organizing Numerical Data. Tables and Charts for Numerical Data. Tables and Charts for Categorical Data. Tabulating and Graphing Bivariate Categorical Data. Graphical Excellence. Summary. Springville Herald Case.
Appendix 2.1. Using Microsoft Excel for Tables and Charts. Appendix 2.2. Using Minitab for Tables and Charts.
3. Summarizing and Describing Numerical Data.
Exploring Numerical Data and Its Properties. Measures of Central Tendency, Variation, and Shape. Exploratory Data Analysis. Obtaining Descriptive Summary Measures from a Population. Recognizing and Practicing Proper Descriptive Summarization and Exploring Ethical Issues. Summary. Case Study: State Alcoholic Beverages Oversight Board Study on Beers. Springville Herald Case.
Appendix 3.1. Using Microsoft Excel for Descriptive Statistics. Appendix 3.2. Using Minitab for Descriptive Statistics.
4. Basic Probability and Probability Distributions.
Basic Probability Concepts. Conditional Probability. Bayes Theorem. The Probability Distribution for a Discrete Random Variable. Binomial Distribution. Poisson Distribution. The Normal Distribution. Assessing the Normality Assumption. The Covariance and its Application in Finance (Optional section). Ethical Issues and Probability. Summary. Springville Herald Case.
Appendix 4.1. Using Microsoft Excel with Probability and Probability Distributions. Appendix 4.2. Using Minitab with Probability Distributions.
5. Sampling Distributions and Confidence Interval Estimation.
Introduction to Sampling Distributions. Introduction to Confidence Interval Estimation. Confidence Interval Estimation for the Mean ( Known). Confidence Interval Estimation for the Mean ( Unknown). Confidence Interval Estimation for the Proportion. Determining Sample Size. Confidence Interval Estimation and Ethical Issues. Summary. Springville Herald Case.
Appendix 5.1. Using Microsoft Excel for Sampling Distributions and Confidence Interval Estimation. Appendix 5.2. Using Minitab for Sampling Distributions and Confidence Interval Estimation.
6. Fundamentals of Hypothesis Testing: One-Sample Tests.
Hypothesis-Testing Methodology. Z Test of Hypothesis for the Mean ( Known). The p-Value Approach to Hypothesis Testing. A Connection between Confidence Interval Estimation and Hypothesis Testing. One-Tailed Tests. t Test of Hypothesis for the Mean ( Unknown). Z Test of Hypothesis for the Proportion. Potential Hypothesis-Testing Pitfalls and Ethical Issues. Summary.
Appendix 6.1. Using Microsoft Excel for One-Sample Tests of Hypothesis. Appendix 6.2. Using Minitab for One-Sample Tests of Hypothesis.
7. Two-Sample and C-Sample Tests with Numerical Data.
Comparing Two Independent Samples: Tests for Differences in Two Means. Comparing Two Related Samples: Tests for the Mean Difference. F Test for Differences in Two Variances. One-Way ANOVA F Test for the Difference in c means. Summary.
Appendix 7.1. Using Microsoft Excel for Two-Sample and c-Sample Tests. Appendix 7.2. Using Minitab for Two-Sample c-Sample Tests. Case Study: Test Marketing and Promoting a Ball-Point Pen. Springville Herald Case.
8. Hypothesis Testing with Categorical Data.
Introduction. Z Test for Differences in Two Proportions. Test for Differences in Two Proportions. Test for Differences in c Proportions. Test of Independence. Summary. Case Study: Airline Satisfaction Survey. Springville Herald Case.
Appendix 8.1. Using Microsoft Excel for Hypothesis Testing with Categorical Data. Appendix 8.2. Using Minitab for Hypothesis Testing with Categorical Data.
9. The Simple Linear Regression Model and Correlation.
Types of Regression Models. Determining the Simple Linear Regression Equation. Measures of Variation in Regression and Correlation. Assumptions of Regression and Correlation. Residual Analysis. Inferences about the Slope. Estimation of Predicted Values. Pitfalls in Regression and Ethical Issues. Computations in Simple Linear Regression. Correlation-Measuring the Strength of the Association. Summary. Case Study: Predicting Sunday Newspaper Circulation. Springville Herald Case.
Appendix 9.1. Using Microsoft Excel for Simple Linear Regression and Correlation. Appendix 9.2. Using Minitab for Simple Linear Regression and Correlation.
10. Multiple Regression.
Developing the Multiple Regression Model. Residual Analysis for the Multiple Regression Model. Testing for the Significance of the Multiple Regression Model. Inferences concerning the Population Regression Coefficients. Testing portions of the Multiple Regression Model. The Curvilinear Regression Model. Dummy-Variable Models. Model Building. Summary. Case Study A: EastSideWestSide Movers. Case Study B: The Mountain States Potato Company.
Appendix 10.1. Using Microsoft Excel for Multiple Linear Regression and Correlation. Appendix 10.2. Using Minitab for Multiple Linear Regression and Correlation.
11. Time Series Analysis.
The Importance of Business Forecasting. Component Factors of the Classical Multiplicative Time-Series Model. Smoothing the Annual Time-Series. Least-Squares Trend Fitting and Forecasting. Autoregressive Modeling for Trend Fitting and Forecasting. Choosing an Appropriate Forecasting Model. Time-Series Forecasting of Monthly or Quarterly data. Pitfalls Concerning Time-Series Analysis and Ethical Considerations. Summary. Case Study Currency Trading. Springville Herald Case.
Appendix 11.1. Using Microsoft Excel for Time Series Analysis. Appendix 11.2. Using Minitab for Time Series Analysis.
12. Statistical Applications in Quality and Productivity Management.
Quality and Productivity: A Historical Perspective. Deming’s Fourteen Points—A Theory of Management by Process. The Theory of Control Charts. Control Chart for the Proportion of Nonconforming Items—The p Chart. The Red Bead Experiment: Understanding Process Variability. Control Charts for the Mean ( ) and the Range (R ). Process Capability. Summary. Case Study: The Harnswell Sewing Machine Company Case. Springville Herald Case.
Appendix 12.1. Using Microsoft Excel for Control Charts. Appendix 12.2. Using Minitab for Control Charts.
ANSWERS TO SELECTED PROBLEMS.
Appendix A. Review of Arithmetic and Algebra.
Appendix B. Summation Notation.
Appendix C. Statistical Symbols and Greek Alphabet.
Appendix D. Documentation for Data Files.
Appendix E. Tables.
Appendix F. Installing the PHStat Add-In.
Index.
