1. Introduction to Statistics and Quality Improvement.

What Is Statistics? Why Study Statistics? Statistical Thinking: Understanding and Managing Variability. Variables, Types of Data, and Levels of Measurement. Operational Definitions. Sampling. Statistical and Spreadsheet Software. Introduction to Quality. A History of Quality and Productivity. Themes of Quality Management. The Connection between Quality and Statistics. Appendix 1.1: Basics of the Windows User Interface. Appendix 1.2: Introduction to Microsoft Excel. Appendix 1.3: Introduction to MINITAB.

2. Tables and Charts.

Introduction and the History of Graphics. Some Tools for Studying a Process: Process Flow Diagrams and Cause-and-Effect Diagrams. The Importance of the Time-Order Plot. Tables and Charts for Numerical Data. Checksheets and Summary Tables. Concentration Diagrams. Graphing Categorical Data. Tables and Charts for Bivariate Categorical Data. Graphical Excellence. Appendix 2.1: Using Microsoft Excel for Tables and Charts. Appendix 2.2: Using MINITAB for Tables and Charts.

3. Describing and Summarizing Data.

Introduction: What's Ahead. Measures of Central Tendency, Variation, and Shape. The Box-and-Whisker Plot. Appendix 3.1: Using Microsoft Excel for Descriptive Statistics. Appendix 3.2: Using MINITAB for Descriptive Statistics.

4. Probability and Discrete Probability Distributions.

Introduction. Some Rules of Probability. The Probability Distribution. The Binomial Distribution. The Hypergeometric Distribution. The Negative Binomial and Geometric Distributions. The Poisson Distribution. Summary and Overview. Appendix 4.1: Using Microsoft Excel for Probability and Probability Distributions. Appendix 4.2: Using MINITAB for Probability and Probability Distributions.

5. Continuous Probability Distributions and Sampling Distributions.

Introduction to Continuous Probability Distributions. The Uniform Distribution. The Normal Distribution. The Standard Normal Distribution as an Approximation to the Binomial and Poisson Distributions. The Normal Probability Plot. The Lognormal Distribution. The Exponential Distribution. The Weibull Distribution. Sampling Distribution of the Mean. Sampling Distribution of the Proportion. Summary. Appendix 5.1: Using Microsoft Excel for Continuous Probability Distributions and Sampling Distributions. Appendix 5.2: Using MINTAB for Continuous Probability Distributions and Sampling Distributions.

6. Process Control Charts I: Basic Concepts and Attribute Charts.

Introduction to Control Charts and Their Applications. Introduction to the Theory of Control Charts. Introduction to Attributes Control Charts. np and p Charts. Area of Opportunity Charts (c Charts and u Charts). Summary. Appendix 6.1: Using Microsoft Excel for Attribute Control Charts. Appendix 6.1: Using Microsoft Excel for Attribute Control Charts. Appendix 6.2: Using MINITAB for Attribute Control Charts.

7. Statistical Process Control Charts II: Variables Control Charts.

Introduction to Variables Control Charts. Rational Subgroups and Sampling Decisions. Control Charts for Central Tendency (X Charts) and Variation (R and s Charts). Control Charts for Individual Values (X Charts). Special Considerations with Variable Charts. The Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) Charts. Process Capability. Summary. Appendix 7.1: Using Microsoft Excel for Variables Control Charts. Appendix 7.2: Using MINITAB for Variables Control Charts.

8. Estimation Procedures.

Introduction. Properties of Estimators. Confidence Interval Estimation of the Mean. Confidence Interval Estimation for the Variance. Prediction Interval Estimate for a Future Individual Value. Tolerance Intervals. Confidence Interval Estimation for the Proportion. Summary. Appendix 8.1: Using Microsoft Excel for Confidence Interval Estimation. Appendix 8.2: Using MINITAB for Confidence Interval Estimation.

9. Introduction to Hypothesis Testing.

Introduction. Basic Concepts of Hypothesis-Testing. One-Sample Tests for the Mean. t Test for the Difference between the Means of Two Independent Groups. Testing for the Difference between Two Variances. The Repeated Measures or Paired t Test. Chi-Square Test for the Differences among Proportions in Two or More Groups. X2 Test of Hypothesis for the Variance or Standard Deviation (Optional Topic). Wilcoxon Rank Sum Test for the Difference between Two Medians (Optional Topic). Summary. Appendix 9.1: Using Microsoft Excel for Hypothesis Testing. Appendix 9.2: Using MINITAB for Hypothesis Testing.

10. The Design of Experiments: One Factor and Randomized Block Experiments.

Introduction and Rationale. Historical Background. The Concept of Randomization. The One-Way Analysis of Variance (ANOVA). The Randomized Block Model. Kruskal-Wallis Rank Test for Differences in c Medians (Optional Topic). Appendix 10.1: Using Microsoft Excel for the Analysis of Variance. Appendix 10.2: Using MINITAB for the Analysis of Variance.

11. The Design of Experiments: Factorial Designs.

Two-Factor Factorial Designs. Factorial Designs Involving Three or More Factors. The Fractional Factorial Design. The Taguchi Approach. Summary and Overview. Appendix 11.1: Using Microsoft Excel for the Two-Factor Factorial Design. Appendix 11.2: Using MINITAB for the Two-Factor Factorial Designs.

12. Simple Linear Regression and Correlation.

Introduction. 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. Confidence and Prediction Interval Estimation. Pitfalls in Regression and Ethical Issues. Computations in Simple Linear Regression. Correlation—Measuring the Strength of the Association. Appendix 12.1: Using Microsoft Excel for Simple Linear Regression and Correlation. Appendix 12.2: Using MINITAB for Simple Linear Regression and Correlation.

13. 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 Quadratic Curvilinear Regression Model. Dummy-Variable Models. Using Transformations in Regressions Models. Collinearity. Model-Building. Pitfalls in Multiple Regression. Appendix 13.1: Using Microsoft Excel for Multiple-Regression Models. Appendix 13.2: Using MINITAB for Multiple Models Regression.

Appendices.

Appendix A: Tables. Appendix B: Statistical Forms. Appendix C: Documentation for the Data Files. Appendix D: Installing the PHStat Microsoft Excel Add-In. Appendix E: Answers to Selected Odd Problems.

Index.