Chapters 1-2: Concepts and Techniques

  1. Understand basic concepts such as treatment factors and experiment units.
  2. Understand common techniques for experiment designs: randomization, replication, and blocking.

Chapters 3-5, One-Way ANOVA

  1. Completely randomized designs.
  2. Identify appropriate statistical models for the design.
  3. Make inferences for contrasts and treatment means.
  4. Calculate simultaneous confidence intervals for contrasts.
  5. Analysis of variance and hypothesis testing about equal treatment effects.
  6. Calculate the power of a test.
  7. Checking model assumptions
  8. Use SAS for statistical analysis.

Chapters 6-7 Two-Way and High-Way ANOVA

  1. Understand the meaning of interaction. 2 Analyze the two-way complete model: ANOVA, hypothesis testing, inferences for contracts and treatment means.
  2. Analyze the two-way main-effects model: ANOVA, hypothesis testing, inferences for contracts and treatment means.
  3. Determine sample sizes.
  4. Analyze small experiments.
  5. Analyze experimental data that involve more than two factors.

Chapter 10: Block Design

  1. How randomization is done.
  2. Analysis: Similar to two-way or multi-way ANOVA.
  3. Simultaneous confidence intervals.
  4. Determining the sample size.

Chapter 11 Incomplete block design

  1. Connected design and connectivity graph.
  2. Balanced incomplete block designs.
  3. Models and analysis of variance.
  4. Multiple comparisons.
  5. Sample size determination.

Chapter 17 Random-Effects and Mixed Models

  1. When to use random effects.
  2. Models for random effects.
  3. Hypothesis testing of random effects.
  4. Choose the correct denominator for the F-test.
  5. Confidence intervals for comparisons of fixed effects;
  6. Use proc mixed and proc glm for the analysis.

Chapter 18 Nested Models

  1. Be able to tell nested factors.
  2. Model and analysis: Fixed effects, random effects, and mixed effects.
  3. Use SAS for statistical analysis.

Chapter 19 Split-plot design

  1. Understand the split-plot designs.
  2. The statistical analysis: Test of the main-effects of the factor assigned to whole plots; multiple comparisons of main-effects of the factor assigned to whole plots; Test of the main-effects of the factor assigned to split plots; multiple comparisons of main-effects of the factor assigned to split plots;
  3. Note the treatments assigned to the split-plots could be factorial (i.e., combinations of the levels of two or more factors). Analysis for the case as covered in Lecture Notes 21.