Class Details

 

·       Module 1: Principles and Techniques

o   Design

o   Analysis

·       Module 2: Planning Experiments

o   A checklist for planning experiments

o   A real experiment

o   Some standard experimental designs

·       Module 3: Designs with One Source of Variation

o   Randomization

o   Model for a completely randomized design

o   Estimation of parameters

o   One-way analysis of variance

o   Sample sizes

o   Using SAS software

o   Using R software

·       Module 4: Inferences for Contrasts and Treatment Means

o   Contrasts

o   Individual contrasts and treatment means

o   Methods of multiple comparisons

o   Sample sizes

o   Using SAS software

o   Using R software

·       Module 5: Checking Model Assumptions

o   Strategy for Checking Model Assumptions          

o   Checking the fit of the model

o   Checking for Outliers

o   Checking Independence of the error terms

o   Checking the equal variance assumption             

o   Checking the normality assumption

o   Using SAS software

o   Using R software

·       Module 6: Experiments with Two Crossed Treatment Factors

o   Models and factorial effects

o   Contrasts

o   Analysis of the two-way complete model

o   Analysis of the two-way main-effects model

o   Calculating sample sizes

o   Small experiments

o   Using SAS software

o   Using R software

·       Module 7: Several Crossed Treatment Factors

o   Models and factorial effects

o   Analysis—Equal sample sizes

o   One observation per cell

o   Using SAS software

o   Using R software            

·       Module 8: Polynomial Regression

o   Models

o   Least squares estimation

o   Test for lack of fit

o   Analysis of the simple linear regression model

o   Analysis of polynomial regression models

o   Orthogonal polynomials and trend contrasts

o   Using SAS software

o   Using R software

·       Module 9: Analysis of Covariance

o   Models

o   Least squares estimates

o   Analysis of covariance

o   Treatment contrasts and confidence intervals

o   Using SAS software

o   Using R software            

·       Module 10: Complete Block Designs

o   Blocks, noise factors or covariates?

o   Design issues    

o   Analysis or randomized complete block designs

o   Analysis of general complete block designs

o   Checking model assumptions

o   Factorial experiments

o   Using SAS software

o   Using R software

·       Module 11: Incomplete Block Designs

o   Design issues

o   Some special incomplete block designs

o   Analysis of general incomplete block designs

o   Factorial experiments   

o   Using SAS software

o   Using R software

·       Module 12: Designs with Two Blocking Factors

o   Design issues

o   Analysis of Row-Column designs

o   Analysis of Latin Square Designs

o   Analysis of Youden Designs

o   Checking the assumptions on the model

o   Factorial experiments in row-column designs

o   Using SAS software

o   Using R software

·       Module 13: Confounded Two-Level Factorial Experiment             

o   Single replicate factorial experiments

o   Confounding using contrasts

o   Confounding using equations

o   Plans for confounded 2p experiments

o   Multireplicate designs

o   Complete confounding: Repeated single-R designs

o   Partial confounding

o   Comparing the multireplicate designs

o   Using SAS software

o   Using R software

·       Module 14: Confounding in General Factorial Experiments

o   Confounding with Factors at Three Levels

o   Designing using pseudofactors

o   Designing confounded asymmetric experiments

o   Using SAS software

o   Using R software

·       Module 15: Fractional Factorial Experiments

o   Fractions from block designs

o   Blocked Fractional factorial experiments

o   Fractions from orthogonal arrays

o   Design for the control of noise variability

o   Small screening designs: orthogonal main effect plans

o   Using SAS software

o   Using R software

·       Module 17: Random Effects and Variance Components

o   One random effect

o   Sample sizes for an experiment with one random effect

o   Checking assumptions on the model

o   Two or more random effects

o   Mixed models

o   Rules for analysis or random-effects and mixed models

o   Block designs and random block effects

o   Using SAS software       

o   Using R software

·       Module 18: Nested Models

o   Examples and models

o   Analysis of nested fixed effects

o   Analysis of nested random effects

o   Using SAS software                                      

o   Using R software

·       Module 19: Split-Plot Designs                          

o   Designs and models

o   Analysis of a split-plot design with complete blocks

o   Split-split-plot designs

o

Course Outline

 

·       Module 1: Principles and Techniques

o   Design

o   Analysis

·       Module 2: Planning Experiments

o   A checklist for planning experiments

o   A real experiment

o   Some standard experimental designs

·       Module 3: Designs with One Source of Variation

o   Randomization

o   Model for a completely randomized design

o   Estimation of parameters

o   One-way analysis of variance

o   Sample sizes

o   Using SAS software

o   Using R software

·       Module 4: Inferences for Contrasts and Treatment Means

o   Contrasts

o   Individual contrasts and treatment means

o   Methods of multiple comparisons

o   Sample sizes

o   Using SAS software

o   Using R software

·       Module 5: Checking Model Assumptions

o   Strategy for Checking Model Assumptions          

o   Checking the fit of the model

o   Checking for Outliers

o   Checking Independence of the error terms

o   Checking the equal variance assumption             

o   Checking the normality assumption

o   Using SAS software

o   Using R software

·       Module 6: Experiments with Two Crossed Treatment Factors

o   Models and factorial effects

o   Contrasts

o   Analysis of the two-way complete model

o   Analysis of the two-way main-effects model

o   Calculating sample sizes

o   Small experiments

o   Using SAS software

o   Using R software

·       Module 7: Several Crossed Treatment Factors

o   Models and factorial effects

o   Analysis—Equal sample sizes

o   One observation per cell

o   Using SAS software

o   Using R software            

·       Module 8: Polynomial Regression

o   Models

o   Least squares estimation

o   Test for lack of fit

o   Analysis of the simple linear regression model

o   Analysis of polynomial regression models

o   Orthogonal polynomials and trend contrasts

o   Using SAS software

o   Using R software

·       Module 9: Analysis of Covariance

o   Models

o   Least squares estimates

o   Analysis of covariance

o   Treatment contrasts and confidence intervals

o   Using SAS software

o   Using R software            

·       Module 10: Complete Block Designs

o   Blocks, noise factors or covariates?

o   Design issues    

o   Analysis or randomized complete block designs

o   Analysis of general complete block designs

o   Checking model assumptions

o   Factorial experiments

o   Using SAS software

o   Using R software

·       Module 11: Incomplete Block Designs

o   Design issues

o   Some special incomplete block designs

o   Analysis of general incomplete block designs

o   Factorial experiments   

o   Using SAS software

o   Using R software

·       Module 12: Designs with Two Blocking Factors

o   Design issues

o   Analysis of Row-Column designs

o   Analysis of Latin Square Designs

o   Analysis of Youden Designs

o   Checking the assumptions on the model

o   Factorial experiments in row-column designs

o   Using SAS software

o   Using R software

·       Module 13: Confounded Two-Level Factorial Experiment             

o   Single replicate factorial experiments

o   Confounding using contrasts

o   Confounding using equations

o   Plans for confounded 2p experiments

o   Multireplicate designs

o   Complete confounding: Repeated single-R designs

o   Partial confounding

o   Comparing the multireplicate designs

o   Using SAS software

o   Using R software

·       Module 14: Confounding in General Factorial Experiments

o   Confounding with Factors at Three Levels

o   Designing using pseudofactors

o   Designing confounded asymmetric experiments

o   Using SAS software

o   Using R software

·       Module 15: Fractional Factorial Experiments

o   Fractions from block designs

o   Blocked Fractional factorial experiments

o   Fractions from orthogonal arrays

o   Design for the control of noise variability

o   Small screening designs: orthogonal main effect plans

o   Using SAS software

o   Using R software

·       Module 17: Random Effects and Variance Components

o   One random effect

o   Sample sizes for an experiment with one random effect

o   Checking assumptions on the model

o   Two or more random effects

o   Mixed models

o   Rules for analysis or random-effects and mixed models

o   Block designs and random block effects

o   Using SAS software       

o   Using R software

·       Module 18: Nested Models

o   Examples and models

o   Analysis of nested fixed effects

o   Analysis of nested random effects

o   Using SAS software                                      

o   Using R software

·       Module 19: Split-Plot Designs                          

o   Designs and models

o   Analysis of a split-plot design with complete blocks

o   Split-split-plot designs

o   Split-plot confounding

o   Using SAS software

o   Using R software

·       Module 20: Computer Experiments

o   Models for computer experiments

o   Gaussian Stochastic Process Model

o   Using SAS software

o   Using R software