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69 Lessons
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I. Introduction
6
1.1
A. Strategy of Experimentation
1.2
B. Some Typical Applications of Experimental Design
1.3
C. Basic Principles
1.4
D. Guidelines for Designing Experiments
1.5
E. A Brief History of Statistical Design
1.6
F. Summary: Using Statistical Techniques in Experimentation
II. Simple Comparative Experiments
6
2.1
A. Introduction
2.2
B. Basic Statistical Concepts
2.3
C. Sampling and Sampling Distributions
2.4
D. Inferences About the Differences in Means, Randomized Designs
2.5
E. Inferences About the Differences in Means, Paired Comparison Designs
2.6
F. Inferences About the Variances of Normal Distributions
III. Experiments with a Single Factor: The Analysis of Variance
10
3.1
A. An Example
3.2
B. The Analysis of Variance
3.3
C. Analysis of the Fixed Effects Model
3.4
D. Model Adequacy Checking
3.5
E. Practical Interpretation of Results
3.6
F. Sample Computer Output
3.7
G. Determining Sample Size
3.8
H. Other Examples of Single-Factor Experiments
3.9
I. The Random Effects Model
3.10
J. Nonparametric Methods in the Analysis of Variance
IV. Randomized Blocks, Latin Squares, and Related Designs
4
4.1
A. The Randomized Complete Block Design
4.2
B. The Latin Square Design
4.3
C. The Graeco-Latin Square Design
4.4
D. Balanced Incomplete Block Designs
V. Introduction to Factorial Designs
3
5.1
A. Basic Definitions and Principles
5.2
B. The Advantage of Factorials
5.3
C. The Two-Factor Factorial Design
VI. Additional Design and Fractional Factorial Designs
6
6.1
A. The 3k Factorial Design
6.2
B. Confounding in the 3k Factorial Design
6.3
C. Fractional Replication of the 3k Factorial Design
6.4
D. Factorials with Mixed Levels
6.5
E. Nonregular Fractional Factorial Designs
6.6
F. Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool
VII. Fitting Regression Models
8
7.1
A. Introduction
7.2
B. Linear Regression Models
7.3
C. Estimation of the Parameters in Linear Regression Models
7.4
D. Hypothesis Testing in Multiple Regression
7.5
E. Confidence Intervals in Multiple Regression
7.6
F. Prediction of New Response Observations
7.7
G. Regression Model Diagnostics
7.8
H. Testing for Lack of Fit
VIII. Response Surface Methods and Designs
7
8.1
A. Introduction to Response Surface Methodology
8.2
B. The Method of Steepest Ascent
8.3
C. Analysis of a Second-Order Response Surface
8.4
D. Experimental Designs for Fitting Response Surfaces
8.5
E. Experiments with Computer Models
8.6
F. Mixture Experiments
8.7
G. Evolutionary Operation
IX. Robust Parameter Design and Process Robustness
5
9.1
A. Introduction
9.2
B. Crossed Array Designs
9.3
C. Analysis of the Crossed Array Design
9.4
D. Combined Array Designs and the Response Model Approach
9.5
E. Choice of Designs
X. Experiments with Random Factors
6
10.1
A. Random Effects Models
10.2
B. The Two-Factor Factorial with Random Factors
10.3
C. The Two-Factor Mixed Model
10.4
D. Rules for Expected Mean Squares
10.5
E. Approximate F-Tests
10.6
F. Some Additional Topics on Estimation of Variance Components
XI. Nested and Split-Plot Designs
5
11.1
A. The Two-Stage Nested Design
11.2
B. The General m-Stage Nested Design
11.3
C. Designs with Both Nested and Factorial Factors
11.4
D. The Split-Plot Design
11.5
E. Other Variations of the Split-Plot Design
XII. Other Design and Analysis Topics
3
12.1
A. Nonnormal Responses and Transformations
12.2
B. Unbalanced Data in a Factorial Design
12.3
C. The Analysis of Covariance
Thiết Kế Thực Nghiệm (DOE)
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