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Design and Analysis of Experiments, 9th Edition.

Author: Douglas C Montgomery
Publisher: New York : Wiley, 2017. ©2017
Edition/Format:   eBook : Document : English : 9th ed
Summary:
TRY (FREE for 14 days), OR RENT this title : www.wileystudentchoice.com Design and Analysis of Experiments, 9th Edition continues to help senior and graduate students in engineering, business, and statistics-as well as working practitioners-to design and analyze experiments for improving the quality, efficiency and performance of working systems. This bestselling text maintains its comprehensive coverage by  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Montgomery, Douglas C.
Design and Analysis of Experiments, 9th Edition.
New York : Wiley, ©2017
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Douglas C Montgomery
ISBN: 9781119299363 1119299365
OCLC Number: 1058228587
Description: 1 online resource (629 pages)
Contents: Cover --
Title Page --
Copyright --
Preface --
Contents --
Chapter 1: Introduction --
1.1 Strategy of Experimentation --
1.2 Some Typical Applications of Experimental Design --
1.3 Basic Principles --
1.4 Guidelines for Designing Experiments --
1.5 A Brief History of Statistical Design --
1.6 Summary: Using Statistical Techniques in Experimentation --
1.7 Problems --
Chapter 2: Simple Comparative Experiments --
2.1 Introduction --
2.2 Basic Statistical Concepts --
2.3 Sampling and Sampling Distributions --
2.4 Inferences About the Differences in Means, Randomized Designs --
2.4.1 Hypothesis Testing --
2.4.2 Confidence Intervals --
2.4.3 Choice of Sample Size --
2.4.4 The Case Where 21 â#x89; Â 22 --
2.4.5 The Case Where 21 and 22 Are Known --
2.4.6 Comparing a Single Mean to a Specified Value --
2.4.7 Summary --
2.5 Inferences About the Differences in Means, Paired Comparison Designs --
2.5.1 The Paired Comparison Problem --
2.5.2 Advantages of the Paired Comparison Design --
2.6 Inferences About the Variances of Normal Distributions --
2.7 Problems --
Chapter 3: Experiments with a Single Factor: The Analysis of Variance --
3.1 An Example --
3.2 The Analysis of Variance --
3.3 Analysis of the Fixed Effects Model --
3.3.1 Decomposition of the Total Sum of Squares --
3.3.2 Statistical Analysis --
3.3.3 Estimation of the Model Parameters --
3.3.4 Unbalanced Data --
3.4 Model Adequacy Checking --
3.4.1 The Normality Assumption --
3.4.2 Plot of Residuals in Time Sequence --
3.4.3 Plot of Residuals Versus Fitted Values --
3.4.4 Plots of Residuals Versus Other Variables --
3.5 Practical Interpretation of Results --
3.5.1 A Regression Model --
3.5.2 Comparisons Among Treatment Means --
3.5.3 Graphical Comparisons of Means --
3.5.4 Contrasts --
3.5.5 Orthogonal Contrasts --
3.5.6 Scheff's Method for Comparing All Contrasts. 3.5.7 Comparing Pairs of Treatment Means --
3.5.8 Comparing Treatment Means with a Control --
3.6 Sample Computer Output --
3.7 Determining Sample Size --
3.7.1 Operating Characteristic and Power Curves --
3.7.2 Confidence Interval Estimation Method --
3.8 Other Examples of Single-Factor Experiments --
3.8.1 Chocolate and Cardiovascular Health --
3.8.2 A Real Economy Application of a Designed Experiment --
3.8.3 Discovering Dispersion Effects --
3.9 The Random Effects Model --
3.9.1 A Single Random Factor --
3.9.2 Analysis of Variance for the Random Model --
3.9.3 Estimating the Model Parameters --
3.10 The Regression Approach to the Analysis of Variance --
3.10.1 Least Squares Estimation of the Model Parameters --
3.10.2 The General Regression Significance Test --
3.11 Nonparametric Methods in the Analysis of Variance --
3.11.1 The Kruskal-Wallis Test --
3.11.2 General Comments on the Rank Transformation --
3.12 Problems --
Chapter 4: Randomized Blocks, Latin Squares, and Related Designs --
4.1 The Randomized Complete Block Design --
4.1.1 Statistical Analysis of the RCBD --
4.1.2 Model Adequacy Checking --
4.1.3 Some Other Aspects of the Randomized Complete Block Design --
4.1.4 Estimating Model Parameters and the General Regression Significance Test --
4.2 The Latin Square Design --
4.3 The Graeco-Latin Square Design --
4.4 Balanced Incomplete Block Designs --
4.4.1 Statistical Analysis of the BIBD --
4.4.2 Least Squares Estimation of the Parameters --
4.4.3 Recovery of Interblock Information in the BIBD --
4.5 Problems --
Chapter 5: Introduction to Factorial Designs --
5.1 Basic Definitions and Principles --
5.2 The Advantage of Factorials --
5.3 The Two-Factor Factorial Design --
5.3.1 An Example --
5.3.2 Statistical Analysis of the Fixed Effects Model --
5.3.3 Model Adequacy Checking --
5.3.4 Estimating the Model Parameters. 5.3.5 Choice of Sample Size --
5.3.6 The Assumption of No Interaction in a Two-Factor Model --
5.3.7 One Observation per Cell --
5.4 The General Factorial Design --
5.5 Fitting Response Curves and Surfaces --
5.6 Blocking in a Factorial Design --
5.7 Problems --
Chapter 6: The 2k Factorial Design --
6.1 Introduction --
6.2 The 22 Design --
6.3 The 23 Design --
6.4 The General 2k Design --
6.5 A Single Replicate of the 2k Design --
6.6 Additional Examples of Unreplicated 2k Designs --
6.7 2k Designs are Optimal Designs --
6.8 The Addition of Center Points to the 2k Design --
6.9 Why We Work with Coded Design Variables --
6.10 Problems --
Chapter 7: Blocking and Confounding in the 2k Factorial Design --
7.1 Introduction --
7.2 Blocking a Replicated 2k Factorial Design --
7.3 Confounding in the 2k Factorial Design --
7.4 Confounding the 2k Factorial Design in Two Blocks --
7.5 Another Illustration of Why Blocking Is Important --
7.6 Confounding the 2k Factorial Design in Four Blocks --
7.7 Confounding the 2k Factorial Design in 2p Blocks --
7.8 Partial Confounding --
7.9 Problems --
Chapter 8: Two-Level Fractional Factorial Designs --
8.1 Introduction --
8.2 The One-Half Fraction of the 2k Design --
8.2.1 Definitions and Basic Principles --
8.2.2 Design Resolution --
8.2.3 Construction and Analysis of the One-Half Fraction --
8.3 The One-Quarter Fraction of the 2k Design --
8.4 The General 2k-p Fractional Factorial Design --
8.4.1 Choosing a Design --
8.4.2 Analysis of 2k-p Fractional Factorials --
8.4.3 Blocking Fractional Factorials --
8.5 Alias Structures in Fractional Factorials and Other Designs --
8.6 Resolution III Designs --
8.6.1 Constructing Resolution III Designs --
8.6.2 Fold Over of Resolution III Fractions to Separate Aliased Effects --
8.6.3 Plackett-Burman Designs --
8.7 Resolution IV and V Designs --
8.7.1 Resolution IV Designs. 8.7.2 Sequential Experimentation with Resolution IV Designs --
8.7.3 Resolution V Designs --
8.8 Supersaturated Designs --
8.9 Summary --
8.10 Problems --
Chapter 9: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs --
9.1 The 3k Factorial Design --
9.1.1 Notation and Motivation for the 3k Design --
9.1.2 The 32 Design --
9.1.3 The 33 Design --
9.1.4 The General 3k Design --
9.2 Confounding in the 3k Factorial Design --
9.2.1 The 3k Factorial Design in Three Blocks --
9.2.2 The 3k Factorial Design in Nine Blocks --
9.2.3 The 3k Factorial Design in 3p Blocks --
9.3 Fractional Replication of the 3k Factorial Design --
9.3.1 The One-Third Fraction of the 3k Factorial Design --
9.3.2 Other 3k-p Fractional Factorial Designs --
9.4 Factorials with Mixed Levels --
9.4.1 Factors at Two and Three Levels --
9.4.2 Factors at Two and Four Levels --
9.5 Nonregular Fractional Factorial Designs --
9.5.1 Nonregular Fractional Factorial Designs for 6, 7, and 8 Factors in 16 Runs --
9.5.2 Nonregular Fractional Factorial Designs for 9 Through 14 Factors in 16 Runs --
9.5.3 Analysis of Nonregular Fractional Factorial Designs --
9.6 Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool --
9.6.1 Design Optimality Criterion --
9.6.2 Examples of Optimal Designs --
9.6.3 Extensions of the Optimal Design Approach --
9.7 Problems --
Chapter 11: Response Surface Methods and Designs --
11.1 Introduction to Response Surface Methodology --
11.2 The Method of Steepest Ascent --
11.3 Analysis of a Second-Order Response Surface --
11.3.1 Location of the Stationary Point --
11.3.2 Characterizing the Response Surface --
11.3.3 Ridge Systems --
11.3.4 Multiple Responses --
11.4 Experimental Designs for Fitting Response Surfaces --
11.4.1 Designs for Fitting the First-Order Model. 11.4.2 Designs for Fitting the Second-Order Model --
11.4.3 Blocking in Response Surface Designs --
11.4.4 Optimal Designs for Response Surfaces --
11.5 Experiments with Computer Models --
11.6 Mixture Experiments --
11.7 Evolutionary Operation --
11.8 Problems --
Chapter 13: Experiments with Random Factors --
13.1 Random Effects Models --
13.2 The Two-Factor Factorial with Random Factors --
13.3 The Two-Factor Mixed Model --
13.4 Rules for Expected Mean Squares --
13.5 Approximate F-Tests --
13.6 Some Additional Topics on Estimation of Variance Components --
13.6.1 Approximate Confidence Intervals on Variance Components --
13.6.2 The Modified Large-Sample Method --
13.7 Problems --
Chapter 14: Nested and Split-Plot Designs --
14.1 The Two-Stage Nested Design --
14.1.1 Statistical Analysis --
14.1.2 Diagnostic Checking --
14.1.3 Variance Components --
14.1.4 Staggered Nested Designs --
14.2 The General m-Stage Nested Design --
14.3 Designs with Both Nested and Factorial Factors --
14.4 The Split-Plot Design --
14.5 Other Variations of the Split-Plot Design --
14.5.1 Split-Plot Designs with More Than Two Factors --
14.5.2 The Split-Split-Plot Design --
14.5.3 The Strip-Split-Plot Design --
14.6 Problems --
Index --
EULA.

Abstract:

TRY (FREE for 14 days), OR RENT this title : www.wileystudentchoice.com Design and Analysis of Experiments, 9th Edition continues to help senior and graduate students in engineering, business, and statistics-as well as working practitioners-to design and analyze experiments for improving the quality, efficiency and performance of working systems. This bestselling text maintains its comprehensive coverage by including: new examples, exercises, and problems (including in the areas of biochemistry and biotechnology); new topics and problems in the area of response surface; new topics in nested and split-plot design; and the residual maximum likelihood method is now emphasized throughout the book.

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