Course Overview

· 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
Schedule
Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 240-667-7757.
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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
