Stella & Rose's Books

Contents

• Preface
• 1 Introduction
• 1.1 Statistics and Data Collection
• 1.2 Beginnings of Statistically Planned Experiments
• 1.3 Definitions and Preliminaries
• 1.4 Purposes of Experimental Design
• 1.5 Types of Experimental Design
• 1.6 Planning Experiments
• 1.7 Performing the Experiments
• 1.8 Use of SAS Software
• 1.9 Review of Important Concepts
• 1.10 Exercises
• 2 Completely Randomized Designs with One Factor
• 2.1 Introduction
• 2.2 Replication and Randomization
• 2.3 A Historical Example
• 2.4 Linear Model for CRD
• 2.5 Verifying Assumptions of the Linear Model
• 2.6 Analysis Strategies When Assumptions are Violated
• 2.7 Determining the Number of Replicates
• 2.8 Comparison of Treatments after the F-Test
• 2.9 Review of Important Concepts
• 2.10 Exercises
• 3 Factorial Design
• 3.1 Introduction
• 3.2 Classical One at a Time versus Factorial Plans
• 3.3 Interpreting Interactions
• 3.4 Creating a Two-Factor Factorial Plan is SAS
• 3.5 Analysis of a Two Factor Factorial in SAS
• 3.6 Factorial Designs with Multiple Factors CRFD
• 3.7 Two Level Factorials
• 3.8 Verifying Assumptions of the Model
• 3.9 Review of Important Concepts
• 3.10 Exercises
• 3.11 Appendix - SAS Macro for Tukey's Single df Test
• 4 Randomized Block Design
• 4.1 Introduction
• 4.2 Creating an RCB in SAS
• 4.3 Model for RCB
• 4.4 An Example of an RCB
• 4.5 Determining the Number of Blocks
• 4.6 Factorial Designs in Blocks
• 4.7 Generalized Complete Block Design
• 4.8 Two Block Factors LSD
• 4.9 Review of Important Concepts
• 4.10 Exercises
• 4.11 Appendix - Data from Golf Experiment
• 5 Designs to Study Variances
• 5.1 Introduction
• 5.2 Random Factors and Random Sampling Experiments
• 5.3 One-Factor Sampling Designs
• 5.4 Estimating Variance Components
• 5.5 Two Factor Sampling Designs
• 5.6 Nested Sampling Experiments (NSE)
• 5.7 Staggered Nested Designs
• 5.8 Designs with Fixed and Random Factors
• 5.9 Graphical Methods of Check Model Assumptions
• 5.10 Review of Important Concepts
• 5.11 Exercises
• 5.12 Appendix
• 6 Fractional Factorial Designs
• 6.1 Introduction
• 6.2 Half-Fractions of 2k designs
• 6.3 Quarter and Higher Fractions of 2k designs
• 6.4 Criteria for Choosing Generators for 2k-p designs
• 6.5 Augmenting Fractional Factorials
• 6.6 Plackett-Burman (PB) Screening Designs
• 6.7 Mixed Level Factorials and Orthogonal Arrays (OA)
• 6.8 Review of Important Concepts
• 6.9 Exercises
• 7 Incomplete and Confounded Block Designs
• 7.1 Introduction
• 7.2 Balanced Incomplete Block (BIB) Designs
• 7.3 Analysis of Incomplete Block Designs
• 7.4 PBIB-BTIB Designs
• 7.5 Youden Square Designs (YSD)
• 7.6 Confounded 2k and 2k-p Designs
• 7.7 Confounding 3 Level and p Level Factorial Designs
• 7.8 Blocking Mixed-Level Factorials and OAs
• 7.9 Partially Confounded Blocked Factorial (PCBF)
• 7.10 Review of Important Concepts
• 7.11 Exercises
• 8 Split-Plot Designs
• 8.1 Introduction
• 8.2 Split-Plot Experiments with CRD in Whole Plots (CRSP)
• 8.3 RCB in Whole Plots RBSP
• 8.4 Analysis Unreplicated 2k Split-Plot Design
• 8.5 2k-p Fractional Factorials in Split Plots (FFSP)
• 8.6 Sample Size and Power Issues for Split-Plot Designs
• 8.7 Review of Important Concepts
• 8.8 Exercises
• 9 Crossover and Repeated Measures Designs
• 9.1 Introduction
• 9.2 Crossover Designs (COD)
• 9.3 Simple AB, BA Crossover Designs for Two Treatments
• 9.4 Crossover Designs for Multiple Treatments
• 9.5 Repeated Measures Designs
• 9.6 Univariate Analysis of Repeated Measures Design
• 9.7 Review of Important Concepts
• 9.8 Exercises
• 10 Response Surface Designs
• 10.1 Introduction
• 10.2 Fundamentals of Response Surface Methodology
• 10.3 Standard Designs for Second Order Models
• 10.4 Creating Standard Designs in SAS
• 10.5 Non-Standard Response Surface Designs
• 10.6 Fitting the Response Surface Model with SAS
• 10.7 Determining Optimum Operating Conditions
• 10.8 Blocked Response Surface (BRS) Designs
• 10.9 Response Surface Split-Plot (RSSP) Designs
• 10.10 Review of Important Concepts
• 10.11 Exercies
• 11 Mixture Experiments
• 11.1 Introduction
• 11.2 Models and Designs for Mixture Experiments
• 11.3 Creating Mixture Designs
• 11.4 Analysis of Mixture Experiment
• 11.5 Constrained Mixture Experiment
• 11.6 Blocking Mixture Experiments
• 11.7 Mixture Experiments with Process Variables
• 11.8 Mixture Experiments in Split-Plot Arrangements
• 11.9 Review of Important Concepts
• 11.10 Exercises
• 11.11 Appendix: Example of Fitting Independent Factors
• 12 Robust Parameter Design Experiments
• 12.1 Introduction
• 12.2 Noise-Sources of Functional Variation
• 12.3 Product Array Parameter Design Experiments
• 12.4 Analysis of Product Array Experiments
• 12.5 Single Array Parameter Design Experiments
• 12.6 Joint Modelling of Mean and Dispersion Effects
• 12.7 Review of Important Concepts
• 12.8 Exercixes
• 13 Experimental Strategies for Increasing Knowledge
• 13,1 Introduction
• 13.2 Sequential Experimentation
• 13.3 One Step Screening and Optimization
• 13.4 Evolutionary Operation
• 13.5 Concluding Remarks
• Bibliography
• Index