Screen LibraryStatistical design and analysis of experiments with applications to engineering and science /
Practicing engineers and scientists often have a need to utilize statistical approaches to solving problems in an experimental setting. Yet many have little formal training in statistics. Statistical Design and Analysis of Experiments gives such readers a carefully selected, practical background in...
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| Main Authors: | |
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| Group Author: | ; |
| Published: |
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| Literature type: | Electronic eBook |
| Language: | English |
| Edition: | 2nd ed. |
| Series: |
Wiley series in probability and statistics
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| Subjects: | |
| Online Access: |
http://onlinelibrary.wiley.com/book/10.1002/0471458503 |
| Summary: |
Practicing engineers and scientists often have a need to utilize statistical approaches to solving problems in an experimental setting. Yet many have little formal training in statistics. Statistical Design and Analysis of Experiments gives such readers a carefully selected, practical background in the statistical techniques that are most useful to experimenters and data analysts who collect, analyze, and interpret data. |
| Carrier Form: | 1 online resource (xix, 728 pages) : illustrations. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
0471458511 9780471458517 1601190514 9781601190512 0471458503 9780471458500 1280366214 9781280366215 |
| Index Number: | TA340 |
| CLC: | TB114 |
| Contents: | Statistics in Engineering and Science -- Fundamentals of Statistical Inference -- Inferences on Means and Standard Deviations -- Statistical Principles in Experimental Design -- Factorial Experiments in Completely Randomized Designs -- Analysis of Completely Randomized Designs -- Fractional Factorial Experiments -- Analysis of Fractional Factorial Experiments -- Experiments in Randomized Block Designs -- Analysis of Designs with Random Factor Levels -- Nested Designs -- Special Designs for Process Improvement -- Analysis of Nested Designs and Designs for Process Improvement -- Linear Regression with One Predictor Variable -- Linear Regression with Several Predictor Variables -- Linear Regression with Factors and Covariates as Predictors -- Designs and Analyses for Fitting Response Surfaces -- Model Assessment -- Variable Selection Techniques. |