实验设计和分析

preface
1. principles and techniques
　1.1. design: basic principles and techniques
　　1.1.1. the art of experimentation
　　1.1.2. replication
　　1.1.3. blocking
　　1.1.4. randomization
　1.2. analysis: basic principles and techniques
2. planning experiments
　2.1. introduction
　2.2. a checklist for planning experiments
　2.3. a real experiment——cotton-spinning experiment
　2.4. some standard experimental designs
　　2.4.1. completely randomized designs
　　2.4.2. block designs
　　2.4.3. designs with two or more blocking factors
　　2.4.4. split-plot designs
　2.5. more real experiments
　　2.5.1. soap experiment
　　2.5.2. battery experiment
　　2.5.3. cake-baking experiment
　exercises
3. designs with one source of variation
　3.1. introduction
　3.2. randomization
　3.3. model for a completely randomized design
　3.4. estimation of parameters
　　3.4.1. estimable functions of parameters
　　3.4.2. notation
　　3.4.3. obtaining least squares estimates
　　3.4.4. properties of least squares estimators
　　3.4.5. estimation ofo2
　　3.4.6. confidence bound for ~r2
　3.5. one-way analysis of variance
　　3.5.1. testing equality of treatment effects
　　3.5.2. use of p-values
　3.6. sample sizes
　　3.6.1. expected mean squares for treatments
　　3.6.2. sample sizes using power of a test
　3.7. a real experiment——-soap experiment, continued
　　3.7.1. checklist, continued
　　3.7.2. data collection and analysis
　　3.7.3. discussion by the experimenter
　　3.7.4. further observations by the experimenter
　3.8. using sas software
　　3.8.1. randomization
　　3.8.2. analysis of variance
　　exercises
4. inferences for contrasts and treatment means
　4.1. introduction
　4.2. contrasts
　　4.2.1. pairwise comparisons
　　4.2.2. treatment versus control
　　4.2.3. difference of averages
　　4.2.4. trends
　4.3. individual contrasts and treatment means
　　4.3.1. confidence interval for a single contrast
　　4.3.2. confidence interval for a single treatment mean
　　4.3.3. hypothesis test for a single contrast or treatment mean
　4.4. methods of multiple comparisons
　　4.4.1. multiple confidence intervals
　　4.4.2. bonferroni method for preplanned comparisons
　　4.4.3. scheff6 method of multiple comparisons
　　4.4.4. tukey method for all pairwise comparisons
　　4.4.5. dunnett method for treatment-versus-control comparisons
　　4.4.6. hsu method for multiple comparisons with the best reatment
　　4.4.7. combination of methods
　　4.4.8. methods not controlling experimentwise error rate
　4.5. sample sizes
　4.6. using sas software
　　4.6.1. inferences on individual contrasts
　　4.6.2. multiple comparisons
　exercises
5. checking model assumptions
　5.1. introduction
　5.2. strategy for checking model assumptions
　　5.2.1. residuals
　　5.2.2. residual plots
　5.3. checking the fit of the model
　5.4. checking for outliers
　5.5. checking independence of the error terms
　5.6. checking the equal variance assumption
　　5.6.1. detection of unequal variances
　　5.6.2. data transformations to equalize variances
　　5.6.3. analysis with unequal error variances
　5.7. checking the normality assumption
　5.8. using sas software
　　5.8.1. using sas to generate residual plots
　　5.8.2. transforming the data
　exercises
6. experiments with two crossed treatment factors
7. several crossed treatment factors
8. polynomial regression
9. analysis of covariance
10. complete block designs
11. incomplete block designs
12. designs with two blocking factors
13. confounded two-level factorial experiments
14. confounding in general factorial experiments
15. fractional factorial experiments
16. esponse surface methodology
17. andom effects and variance components
18. estde models
19. plit-plot designs
a. ables
bibliography
index of authors
index of experiments
index of subjects