First Course in Design and Analysis of Experiments

ISBN-10: 0716735105

ISBN-13: 9780716735106

Edition: 2000

Authors: Gary W. Oehlert
List price: $128.99
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Description: This text on preparing and interpreting experimental data places equal emphasis on the design and analysis of experiments. It gives examples drawn from agriculture, engineering, education, mathematics, business, social science and health sciences.

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Book details

List price: $128.99
Copyright year: 2000
Publisher: W. H. Freeman & Company
Publication date: 1/19/2000
Binding: Hardcover
Pages: 600
Size: 7.50" wide x 9.50" long x 1.75" tall
Weight: 2.992

Why Experiment?
Components of an Experiment
Terms and Concepts
More About Experimental Units
More About Responses
Randomization and Design
Randomization Against Confounding
Randomizing Other Things
Performing a Randomization
Randomization for Inference
The paired t-test
Two-sample t-test
Randomization inference and standard inference
Further Reading and Extensions
Completely Randomized Designs
Structure of a CRD
Preliminary Exploratory Analysis
Models and Parameters
Estimating Parameters
Comparing Models: The Analysis of Variance
Mechanics of ANOVA
Why ANOVA Works
Back to Model Comparison
Side-by-Side Plots
Dose-Response Modeling
Further Reading and Extensions
Looking for Specific Differences--Contrasts
Contrast Basics
Inference for Contrasts
Orthogonal Contrasts
Polynomial Contrasts
Further Reading and Extensions
Multiple Comparisons
Error Rates
Bonferroni-Based Methods
The Scheffe Method for All Contrasts
Pairwise Comparisons
Displaying the results
The Studentized range
Simultaneous confidence intervals
Strong familywise error rate
False discovery rate
Experimentwise error rate
Comparisonwise error rate
Pairwise testing reprise
Pairwise comparisons methods that do not control combined Type I error rates
Confident directions
Comparison with Control or the Best
Comparison with a control
Comparison with the best
Reality Check on Coverage Rates
A Warning About Conditioning
Some Controversy
Further Reading and Extensions
Checking Assumptions
Assessing Violations of Assumptions
Assessing nonnormality
Assessing nonconstant variance
Assessing dependence
Fixing Problems
Accommodating nonnormality
Accommodating nonconstant variance
Accommodating dependence
Effects of Incorrect Assumptions
Effects of nonnormality
Effects of nonconstant variance
Effects of dependence
Implications for Design
Further Reading and Extensions
Power and Sample Size
Approaches to Sample Size Selection
Sample Size for Confidence Intervals
Power and Sample Size for ANOVA
Power and Sample Size for a Contrast
More about Units and Measurement Units
Allocation of Units for Two Special Cases
Further Reading and Extensions
Factorial Treatment Structure
Factorial Structure
Factorial Analysis: Main Effect and Interaction
Advantages of Factorials
Visualizing Interaction
Models with Parameters
The Analysis of Variance for Balanced Factorials
General Factorial Models
Assumptions and Transformations
Single Replicates
Pooling Terms into Error
A Closer Look at Factorial Data
Contrasts for Factorial Data
Modeling Interaction
Interaction plots
One-cell interaction
Quantitative factors
Tukey one-degree-of-freedom for nonadditivity
Further Reading and Extensions
Further Topics in Factorials
Unbalanced Data
Sums of squares in unbalanced data
Building models
Testing hypotheses
Empty cells
Multiple Comparisons
Power and Sample Size
Two-Series Factorials
Single replicates
Further Reading and Extensions
Random Effects
Models for Random Effects
Why Use Random Effects?
ANOVA for Random Effects
Approximate Tests
Point Estimates of Variance Components
Confidence Intervals for Variance Components
Further Reading and Extensions
Nesting, Mixed Effects, and Expected Mean Squares
Nesting Versus Crossing
Why Nesting?
Crossed and Nested Factors
Mixed Effects
Choosing a Model
Hasse Diagrams and Expected Mean Squares
Test denominators
Expected mean squares
Constructing a Hasse diagram
Variances of Means and Contrasts
Unbalanced Data and Random Effects
Staggered Nested Designs
Complete Block Designs
The Randomized Complete Block Design
Why and when to use the RCB
Analysis for the RCB
How well did the blocking work?
Balance and missing data
Latin Squares and Related Row/Column Designs
The crossover design
Randomizing the LS design
Analysis for the LS design
Replicating Latin Squares
Efficiency of Latin Squares
Designs balanced for residual effects
Graeco-Latin Squares
Further Reading and Extension
Incomplete Block Designs
Balanced Incomplete Block Designs
Intrablock analysis of the BIBD
Interblock information
Row and Column Incomplete Blocks
Partially Balanced Incomplete Blocks
Cyclic Designs
Square, Cubic, and Rectangular Lattices
Alpha Designs
Further Reading and Extension
Factorials in Incomplete Blocks--Confounding
Confounding the Two-Series Factorial
Two blocks
Four or more blocks
Analysis of an unreplicated confounded two-series
Replicating a confounded two-series
Double confounding
Confounding the Three-Series Factorial
Building the design
Confounded effects
Analysis of confounded three-series
Further Reading and Extensions
Split-Plot Designs
What Is a Split Plot?
Fancier Split Plots
Analysis of a Split Plot
Split-Split Plots
Other Generalizations of Split Plots
Repeated Measures
Crossover Designs
Further Reading and Extensions
Designs with Covariates
The Basic Covariate Model
When Treatments Change Covariates
Other Covariate Models
Further Reading and Extensions
Fractional Factorials
Why Fraction?
Fractioning the Two-Series
Analyzing a 2[superscript k-1]
Resolution and Projection
Confounding a Fractional Factorial
Sequences of Fractions
Fractioning the Three-Series
Problems with Fractional Factorials
Using Fractional Factorials in Off-Line Quality Control
Designing an off-line quality experiment
Analysis of off-line quality experiments
Further Reading and Extensions
Response Surface Designs
Visualizing the Response
First-Order Models
First-Order Designs
Analyzing First-Order Data
Second-Order Models
Second-Order Designs
Second-Order Analysis
Mixture Experiments
Designs for mixtures
Models for mixture designs
Further Reading and Extensions
On Your Own
Experimental Context
Experiments by the Numbers
Final Project
Linear Models for Fixed Effects
Least Squares
Comparison of Models
Random Variation
Estimable Functions
The Scheffe Method
Experimental Design Plans
Latin Squares
Standard Latin Squares
Orthogonal Latin Square
Balanced Incomplete Block Designs
Efficient Cyclic Designs
Alpha Designs
Two-Series Confounding and Fractioning Plans
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