This introductory page refers to various documents that are a complement to the class notes for this course.

There are examples from the course notes, plus other examples illustrating various topics. Most examples have 3 sections :

1. Description of topic and data
2. SAS Code
3. Interpretation, further references, pointers, etc

You can browse through topics as well as retrieve files with the data from examples, together with the SAS code used. The course notes are available as as a PDF file that can be read and printed using Adobe Acrobat reader.

Topics

Introduction

• Introduction, SAS, viewing
• Prerequisites/Review
• Introduction to the SAS language
• The Model, what and why

Multiple Regression and Correlations

• Multiple Regression I
• Are We Normal?
• Type I and Type III (Sequential and Marginal) Sums of Squares
• Quadratic Regressions
• Independent variables, To Be (In), or Not To Be (In)?
• Computing our own F-values
• Correlations

Classification Models

• Completely Randomized Design, One-way classification
• Partial R²
• Multiple Comparisons
• Fixed or Random Effects
• Regression vs. Classification
• Variance, Normality and Homogeneity of Variance (The Sequel)
• Two-way classification, Fixed effects
• Gains in efficiency
• Ignoring an effect from a model (Ignorance is bliss!)
• Nested designs
• Nested designs (the sequel)
• Factorial designs, fixed effects
• Classification and covariates
• Factorial models, Classification vs. Regression

Mixed Models

• What are they and why are they important?
• Two-way classification, mixed model (RCBD)
• Pseudo R² in a mixed model
• Repeated measures
• Latin square Designs
• Simple Cross-Over Design

Other Aspects

• Expectations of Mean Squares (CRD)
• Sample size and number of sub-samples
• Combining Probabilities
• Categorical Data

Examples

• Log of examples
• Output from example 1
• Output from example 2
• Output from example 3

R stuff

• Link to various examples using R

• Selected references
• Course notes (PDF, Acrobat format)

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