STAT 345: Design and Analysis of Experiments

Longhai Li, Department of Mathematics and Statistics, University of Saskatchewan

An introduction to the principles of experimental design and analysis of variance based on linear models. Topics includes: randomization, blocking, factorial experiments, confounding, random effects, analysis of covariance. Emphasis will be on fundamental principles and data analysis techniques using R rather than on mathematical theory. The following is a list of demonstration HTML produced with Rmarkdown.

1. Introduction and R Basics (introduction to statistics, R, import data into R)

2. Analysis of Simple Comparative Experiments (paired t-test)

3. Simple Linear Regression

4. One-way ANOVA Based on Linear Models (Analyzing Completely Randomized Design with a Single Categorial Factor)

5. Design and Analysis of Experiments with Block Factors

6. General Factorial Design

7. Full 2^k Factorial Design

8. Two-Level Fractional Factorial Design

9. Introduction Response Surface Design

Rmarkdown and other supporting files for producing the above html files can be found from this Github folder. Some examples are taken from the textbook Design and Analysis of Experiments by Douglas C. Montgomery.

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