# Statistical Design and Analysis of Experiments

 Автор(ы): Mason Robert L. 06.10.2007 Год изд.: 2003 Описание: Statistical Design and Analysis of Experiments is intended to be a practitioner's guide to statistical methods for designing and analyzing experiments. The topics selected for inclusion in this book represent statistical techniques that we feel are most useful to experimenters and data analysts who must either collect, analyze, or interpret data. The material included in this book also was selected to be of value to managers, supervisors, and other administrators who must make decisions based in part on the analyses of data that may have been performed by others. Оглавление: PART I FUNDAMENTAL STATISTICAL CONCEPTS    1. Statistics in Engineering and Science      1.1. The Role of Statistics in Experimentation      1.2. Populations and Samples      1.3. Parameters and Statistics      1.4. Mathematical and Statistical Modeling        Exercises    2. Fundamentals of Statistical Inference      2.1. Traditional Summary Statistics      2.2. Statistical Inference      2.3. Probability Concepts      2.4. Interval Estimation      2.5. Statistical Tolerance Intervals      2.6. Tests of Statistical Hypotheses      2.7. Sample Size and Power        Appendix: Probability Calculations        Exercises    3. Inferences on Means and Standard Deviations      3.1. Inferences on a Population or Process Mean        3.1.1. Confidence Intervals        3.1.2. Hypothesis Tests        3.1.3. Choice of a Confidence Interval or a Test        3.1.4. Sample Size      3.2. Inferences on a Population or Process Standard Deviation        3.2.1. Confidence Intervals        3.2.2. Hypothesis Tests      3.3. Inferences on Two Populations or Processes Using Independent Pairs of Correlated Data Values      3.4. Inferences on Two Populations or Processes Using Data from Independent Samples      3.5. Comparing Standard Deviations from Several Populations        Exercises  PART II DESIGN AND ANALYSIS WITH FACTORIAL STRUCTURE    4. Statistical Principles in Experimental Design      4.1. Experimental-Design Terminology      4.2. Common Design Problems        4.2.1. Masking Factor Effects        4.2.2. Uncontrolled Factors        4.2.3. Erroneous Principles of Efficiency        4.2.4. One-Factor-at-a-Time Testing      4.3. Selecting a Statistical Design        4.3.1. Consideration of Objectives        4.3.2. Factor Effects        4.3.3. Precision and Efficiency        4.3.4. Randomization      4.4. Designing for Quality Improvement        Exercises    5. Factorial Experiments in Completely Randomized Designs      5.1. Factorial Experiments      5.2. Interactions      5.3. Calculation of Factor Effects      5.4. Graphical Assessment of Factor Effects        Appendix: Calculation of Effects for Factors with More than Two Levels        Exercises    6. Analysis of Completely Randomized Designs      6.1. Balanced Multifactor Experiments        6.1.1. Fixed Factor Effects        6.1.2. Analysis-of-Variance Models        6.1.3. Analysis-of-Variance Tables      6.2. Parameter Estimation        6.2.1. Estimation of the Error Standard Deviation        6.2.2. Estimation of Effects Parameters        6.2.3. Quantitative Factor Levels      6.3. Statistical Tests        6.3.1. Tests on Individual Parameters        6.3.2. F-Tests for Factor Effects      6.4. Multiple Comparisons        6.4.1. Philosophy of Mean-Comparison Procedures        6.4.2. General Comparisons of Means        6.4.3. Comparisons Based on f-Statistics        6.4.4. Tukey's Significant Difference Procedure      6.5. Graphical Comparisons        Exercises    7. Fractional Factorial Experiments      7.1. Confounding of Factor Effects      7.2. Design Resolution      7.3. Two-Level Fractional Factorial Experiments        7.3.1. Half Fractions        7.3.2. Quarter and Smaller Fractions      7.4. Three-Level Fractional Factorial Experiments        7.4.1. One-Third Fractions        7.4.2. Orthogonal Array Tables      7.5. Combined Two- and Three-Level Fractional Factorials      7.6. Sequential Experimentation        7.6.1. Screening Experiments        7.6.2. Designing a Sequence of Experiments        Appendix: Fractional Factorial Design Generators        Exercises    8. Analysis of Fractional Factorial Experiments      8.1. A General Approach for the Analysis of Data from Unbalanced Experiments      8.2. Analysis of Marginal Means for Data from Unbalanced Designs      8.3. Analysis of Data from Two-Level, Fractional Factorial Experiments      8.4. Analysis of Data from Three-Level, Fractional Factorial Experiments      8.5. Analysis of Fractional Factorial Experiments with Combinations of Factors Having Two and Three Levels      8.6. Analysis of Screening Experiments        Exercises  PART III Design and Analysis with Random Effects    9. Experiments in Randomized Block Designs      9.1. Controlling Experimental Variability      9.2. Complete Block Designs      9.3. Incomplete Block Designs        9.3.1. Two-Level Factorial Experiments        9.3.2. Three-Level Factorial Experiments        9.3.3. Balanced Incomplete Block Designs      9.4. Latin-Square and Crossover Designs        9.4.1. Latin Square Designs        9.4.2. Crossover Designs        Appendix: Incomplete Block Design Generators        Exercises    10. Analysis of Designs with Random Factor Levels      10.1. Random Factor Effects      10.2. Variance-Component Estimation      10.3. Analysis of Data from Block Designs        10.3.1. Complete Blocks        10.3.2. Incomplete Blocks      10.4. Latin-Square and Crossover Designs        Appendix: Determining Expected Mean Squares        Exercises    11. Nested Designs      11.1. Crossed and Nested Factors      11.2. Hierarchically Nested Designs      11.3. Split-Plot Designs        11.3.1. An Illustrative Example        11.3.2. Classical Split-Plot Design Construction      11.4. Restricted Randomization        Exercises    12. Special Designs for Process Improvement      12.1. Assessing Quality Performance        12.1.1. Gage Repeatability and Reproducibility        12.1.2. Process Capability      12.2. Statistical Designs for Process Improvement        12.2.1. Taguchi's Robust Product Design Approach        12.2.2. An Integrated Approach        Appendix: Selected Orthogonal Arrays        Exercises    13. Analysis of Nested Designs and Designs for Process Improvement      13.1. Hierarchically Nested Designs      13.2. Split-Plot Designs      13.3. Gage Repeatability and Reproducibility Designs      13.4. Signal-to-Noise Ratios        Exercises  PART IV Design and Analysis with Quantitative Predictors and Factors    14. Linear Regression with One Predictor Variable      14.1. Uses and Misuses of Regression      14.2. A Strategy for a Comprehensive Regression Analysis      14.3. Scatterplot Smoothing      14.4. Least-Squares Estimation        14.4.1. Intercept and Slope Estimates        14.4.2. Interpreting Least-Squares Estimates        14.4.3. No-Intercept Models        14.4.4. Model Assumptions      14.5. Inference        14.5.1. Analysis-of-Variance Table        14.5.2. Tests and Confidence Intervals        14.5.3. No-Intercept Models        14.5.4. Intervals for Responses        Exercises    15. Linear Regression with Several Predictor Variables      15.1. Least Squares Estimation        15.1.1. Coefficient Estimates        15.1.2. Interpreting Least-Squares Estimates      15.2. Inference        15.2.1. Analysis of Variance        15.2.2. Lack of Fit        15.2.3. Tests on Parameters        15.2.4. Confidence Intervals      15.3. Interactions Among Quantitative Predictor Variables      15.4. Polynomial Model Fits        Appendix: Matrix Form of Least-Squares Estimators        Exercises    16. Linear Regression with Factors and Covariates as Predictors      16.1. Receding Categorical Predictors and Factors        16.1.1. Categorical Variables: Variables with Two Values        16.1.2. Categorical Variables: Variables with More Than Two Values        16.1.3. Interactions      16.2. Analysis of Covariance for Completely Randomized Designs      16.3. Analysis of Covariance for Randomized Complete Block Designs        Appendix: Calculation of Adjusted Factor Averages        Exercises    17. Designs and Analyses for Fitting Response Surfaces      17.1. Uses of Response-Surface Methodology      17.2. Locating an Appropriate Experimental Region      17.3. Designs for Fitting Response Surfaces        17.3.1. Central Composite Design        17.3.2. Box-Behnken Design        17.3.3. Some Additional Designs      17.4. Fitting Response-Surface Models        17.4.1. Optimization        17.4.2. Optimization for Robust Parameter Product-Array Designs        17.4.3. Dual Response Analysis for Quality Improvement Designs        Appendix: Box-Behnken Design Plans; Locating Optimum Responses        Exercises    18. Model Assessment      18.1. Outlier Detection        18.1.1. Univariate Techniques        18.1.2. Response-Variable Outliers        18.1.3. Predictor-Variable Outliers      18.2. Evaluating Model Assumptions        18.2.1. Normally Distributed Errors        18.2.2. Correct Variable Specification        18.2.3. Nonstochastic Predictor Variables      18.3. Model Respecification        18.3.1. Nonlinear-Response Functions        18.3.2. Power Reexpressions        Appendix: Calculation of Leverage Values and Outlier Diagnostics        Exercises    19. Variable Selection Techniques      19.1. Comparing Fitted Models      19.2. All-Possible-Subset Comparisons      19.3. Stepwise Selection Methods        19.3.1. Forward Selection        19.3.2. Backward Elimination        19.3.3. Stepwise Iteration      19.4. Collinear Effects        Appendix: Cryogenic-Flowmeter Data        Exercises  APPENDIX: Statistical Tables    1. Table of Random Numbers    2. Standard Normal Cumulative Probabilities    3. Student t Cumulative Probabilities    4. Chi-Square Cumulative Probabilities    5. F Cumulative Probabilities    6. Factors for Determining One-sided Tolerance Limits    7. Factors for Determining Two-sided Tolerance Limits    8. Upper-Tail Critical Values for the F-Max Test    9. Orthogonal Polynomial Coefficients    10. Critical Values for Outlier Test Using (?) and (?)    11. Critical Values for Outlier Test Using (?)    12. Coefficients Used in the Shapiro-Wilk Test for Normality    13. Critical Values for the Shapiro-Wilk Test for Normality    14. Percentage Points of the Studentized Range  INDEX 723 Формат: djvu Размер: 5453371 байт Язык: ENG Рейтинг: 148 Открыть: Нет поддержки JS :(