Statistics and Econometrics or Modern Statistics for Engineering and Quality Improvement

Statistics and Econometrics: Methods and Applications

Author: David J Zimmerman

Every major econometric method is illustrated by a persuasive, real life example applied to real data.
* Explores subjects such as sample design, which are critical to practical application econometrics.

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Modern Statistics for Engineering and Quality Improvement

Author: John S Lawson

Through years of teaching experience, John S. Lawson and John Erjavec have learned that it doesn't take much theoretical background before engineers can learn practical methods of data collections, analysis, and interpretation that will be useful in real life and on the job. With this premise in mind, the authors wrote ENGINEERING AND INDUSTRIAL STATISTICS, which includes the basic topics of engineering statistics but puts less emphasis on the theoretical concepts and elementary topics usually found in an introductory statistics book. Instead, the authors put more emphasis on techniques that will be useful for engineers. With fewer details of traditional probability and inference and more emphasis on the topics useful to engineers, the book is flexible for instructors and interesting for students.

Booknews

A textbook for a first course in statistics for undergraduate engineering majors. Lawson (Brigham Young University) and Erjavee (University of North Dakota) concentrate on methods for collecting, analyzing, and interpreting data in industrial studies, and introduce tools such as experimental designs, control charts, and variance analysis. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Table of Contents:

Pt. I	Introduction	1
1	The Scientific Method and Statistics	3
2	Concepts of Quality Control	23
Pt. II	Basic Tools	47
3	Theoretical Background: Probability and Statistics	49
4	Descriptive Tools	91
5	Probability Plots	119
6	Inferential Statistics, Prediction and Statistical Decision Rules	139
Pt. III	Good Experiments Make for Good Statistics	179
7	Strategies for Experimentation with Multiple Factors	181
8	Basic Two-Level Factorial Experiments	199
9	Additional Tools for Design and Analysis of Two-Level Factorials	249
10	Regression Analysis	291
11	Multiple Level Factorial Experiments	341
12	Screening Designs	399
Pt. IV	Optimization Experiments	463
13	Response Surface Methodology	465
14	Response Surface Model Fitting	493
15	Mixture Experiments	519
Pt. V	Variability and Quality	583
16	Characterizing Variability in Data	585
17	Shewhart Control Charts	637
18	Off-Line Quality Control and Robust Design	705
	App. A	747
	App. B	757
	Index	805