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Synopsis
STATISTICAL METHODS FOR ENGINEERS by Geoff Vining and Scott Kowalski presents real engineering data and takes a truly modern approach to statistics. An engineering case study runs throughout the text and gives conceptual continuity through each chapter. An excellent opening introduces students to the connection and the intimate link between statistical decision making and engineering.
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Review
1. ENGINEERING METHOD AND DATA COLLECTION. Need for Statistical Methods in Engineering. Engineering Method and Statistical Thinking. Statistical Thinking and Structured Problem Solving. Models. Obtaining Data. Sampling. Basic Principles of Experimental Design. Examples of Engineering Experiments. Purpose of Engineering Statistics. Case Study: Manufacture of Writing Instruments. Ideas for Projects References. 2. DATA DISPLAYS. Importance of Data Displays. Stem-and-Leaf Displays. Boxplots. Using Computer Software. Using Boxplots to Analyze Designed Experiments. Case Study. Need for Probability and Distributions. Ideas for Projects References. 3. MODELING RANDOM BEHAVIOR. Probability. Random Variables and Distributions. Discrete Random Variables. Continuous Random Variables. The Normal Distribution. Random Behavior of Means. Random Behavior of Means When the Variance Is Unknown. Normal Approximation to the Binomial. The Weibull Distribution for Reliability Applications. Case Study References. 4. ESTIMATION AND TESTING. Estimation. Hypothesis Testing. Inference for a Single Mean. Inference for a Single Proportion. Inference for Two Independent Samples. The Paired t-Test. Inference for Two Proportions. Inference for Variances. Transformations and Nonparametric Analyses. Case Study. Ideas for Projects References. 5. CONTROL CHARTS AND STATISTICAL PROCESS CONTROL. Overview. Specification Limits. X- and R-Charts. X- and s^2-Charts. X-Chart. np-Chart. c-Chart. Average Run Lengths. Standard Control Charts with Runs Rules. CUSUM and EWMA Charts. Basic Process Capability Indices. The SPC Approach to Gage R Studies. Case Study. Ideas for Projects References. 6. LINEAR REGRESSION ANALYSIS. Relationships Among Data. Simple Linear Regression. Multiple Linear Regression. Residual Analysis. Collinearity Diagnostics. Case Study. Ideas for Projects References. 7. INTRODUCTION TO 2k FACTORIAL-BASED EXPERIMENTS. The 2^2 Factorial Design. The 2k Factorial Design. Fractions of the 2k Factorial Design. Case Study. Ideas for Projects References. 8. INTRODUCTION TO RESPONSE SURFACE METHODOLOGY. Sequential Philosophy of Experimentation. Central Composite Designs. Box-Behnken Designs. Multiple Responses. Experimental Designs for Quality Improvement. Case Study. Ideas for Projects References. 9. CODA. The Themes of This Course. Integrating the Themes. Statistics and Engineering. Appendix. Tables.
About the Author
Dr. Geoffrey Vining received his Ph.D. from Virginia Tech., Blacksburg. He is a Professor and Department Head in the Statistics Department at Virginia Tech. He also served on the faculty of the Statistics Department at the University of Florida, Gainesville, as a practicing engineer with the Faber-Castell Corporation and as an industrial consultant.
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