To the Instructor.

Sample Exam Questions.

To the Student.

Acknowledgments.

**1. Introduction to Experimental Design.**

1. The Challenge of planning a good experiment.

2. Three basic principles and four experimental designs.

3. The factor structure of the four experimental designs.

**2. Informal Analysis and Checking Assumptions.**

1. What analysis of variance does.

2. The six fisher assumptions.

3. Informal analysis, part 1: parallel dot graphs and choosing a scale.

4. Informal analysis, part 2: interaction graph for the log concentrations.

**3. Formal Anova: Decomposing the Data and Measuring Variability, Testing Hypothesis and Estimating True Differences.**

1. Decomposing the data.

2. Computing mean squares to measure average variability.

3. Standard deviation = root mean square for residuals.

4. Formal hypothesis testing: are the effects detectable?

5. Confidence intervals: the likely size of true differences.

**4. Decisions About the Content of an Experiment.**

1. The response.

2. Conditions.

3. Material.

**5. Randomization and the Basic Factorial Design.**

1. The basic factorial design (“What you do”).

2. Informal analysis.

3. Factor structure (“What you get”).

4. Decomposition and analysis of variance for one-way BF designs.

5. Using a computer [Optional].

6. Algebraic notation for factor structure [Optional].

**6. Interaction and the Principle of Factorial Crossing.**

1. Factorial crossing and the two-way basic factorial design, or BF[2].

2. Interaction and the interaction graph.

3. Decomposition and ANOVA for the two-way design.

4. Using a computer [Optional].

5. Algebraic notation for the two-way BF design [Optional].

**7. The Principle of Blocking.**

1. Blocking and the complete block design (CB).

2. Two nuisance factors: the Latin square design(LS).

3. The split plot/repeated measures design (SP/RM).

4. Decomposition and analysis of variance.

5. Scatterplots for data sets with blocks.

6. Using a computer. [Optional].

7. Algebraic notation for the CB, LS And SP/RM Designs.

**8. Working with the Four Basic Designs.**

1. Comparing and recognizing design structures.

2. Choosing a design structure: deciding about blocking.

3. Informal analysis: examples.

4. Recognizing alternative to ANOVA.

**9. Extending the Basic Designs by Factorial Crossing.**

1. Extending the BF design: general principles.

2. Three or more crossed factors of interest.

3. Compound within-blocks factors.

4.Graphical methods for 3-factor interactions.

5. Analysis of variance.

**10. Decomposing a Data Set.**

1. The basic decomposition step and the BF[1] design.

2. Decomposing data from balanced designs.

**11. Comparisons, Contrasts, and Confidence Intervals.**

1. Comparisons: confidence intervals and tests.

2. Adjustments for multiple comparisons.

3. Between-blocks factors and compound within-blocks factors.

4. Linear estimators and orthogonal contrasts [Optional].

**12. The Fisher Assumptions and How to Check Them.**

1. Same SDs (s).

2. Independent chance errors (I).

3. The normality assumption (N).

4. Effects are additive (A) and constant (C).

5. Estimating replacement values for outliers.

**13. Other Experimental Designs and Models.**

1. New factor structures built by crossing and nesting.

2. New uses for old factor structures: fixed versus random effects.

3. Models with mixed interaction effects.

4. Expected mean square and f-ratios.

**14. Continuous Carriers: A Visual Approach to Regression, Correlation and Analysis of Covariance.**

1. Regression.

2. Balloon summaries and correlation.

3. Analysis of covariance.

**15. Sampling Distributions and the Role of the Assumptions.**

1. The logic of hypothesis testing.

2. Ways to think about sampling distributions.

3. Four fundamental families of distributions.

4. Sampling distributions for linear estimators.

5. Approximate sampling distributions for f-ratios.

6. Why (and when) are the models reasonable?

Tables.

Data Sources.

Subject Index.

Examples.