Statistical Methods for Engineers

Vining, G. Geoffrey; Kowalski, Scott

ISBN 10: 053873518X / ISBN 13: 9780538735186

Published by Cengage Learning (edition 3), 2010

About this Item

The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

Seller Inventory # 053873518X-7-1

Bibliographic Details

• Title: Statistical Methods for Engineers
• Publisher: Cengage Learning (edition 3)
• Publication Date: 2010
• Language: English
• ISBN 10: 053873518X
• ISBN 13: 9780538735186
• Binding: Hardcover
• Edition: 3.
• Condition: Fair

About this title

Synopsis

STATISTICAL METHODS FOR ENGINEERS offers a balanced, streamlined one-semester introduction to Engineering Statistics that emphasizes the statistical tools most needed by practicing engineers. Using real engineering problems with real data based on actual journals and consulting experience in the field, users see how statistics fits within the methods of engineering problem solving. The book teaches users how to think like an engineer at analyzing real data and planning a project the same way they will in their careers.

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.