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JMP 8 Design of Experiments Guide, Second Edition [Pehme köide]

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  • Formaat: Paperback / softback, 292 pages, kõrgus x laius x paksus: 235x191x15 mm, kaal: 507 g, black & white illustrations
  • Ilmumisaeg: 18-Dec-2009
  • Kirjastus: SAS Publishing
  • ISBN-10: 1607643030
  • ISBN-13: 9781607643036
Teised raamatud teemal:
JMP 8 Design of Experiments Guide, Second EditionSuurem pilt
  • Formaat: Paperback / softback, 292 pages, kõrgus x laius x paksus: 235x191x15 mm, kaal: 507 g, black & white illustrations
  • Ilmumisaeg: 18-Dec-2009
  • Kirjastus: SAS Publishing
  • ISBN-10: 1607643030
  • ISBN-13: 9781607643036
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781607643036.html
Märksõnad:
The JMP 8 Design of Experiments Guide, Second Edition, contains information about the JMP Design of Experiments (DOE) platform, including the JMP Custom Designer. This book covers a wide variety of designs including screening, response surface, full factorial, discrete choice, space-filling, non-linear, Taguchi, augmented, mixture designs, and more. The second edition has been updated to reflect software updates.
1 Introduction to Designing Experiments
A Beginner's Tutorial
1(2)
About Designing Experiments
3(1)
My First Experiment
3(10)
The Situation
3(1)
Step 1 Design the Experiment
3(3)
Step 2 Define Factor Constraints
6(1)
Step 3 Add Interaction Terms
6(1)
Step 4 Determine the Number of Runs
7(1)
Step 5 Check the Design
7(2)
Step 6 Gather and Enter the Data
9(1)
Step 7 Analyze the Results
10(3)
2 Examples Using the Custom Designer
13(40)
Creating Screening Experiments
15(14)
Creating a Main-Effects-Only Screening Design
15(1)
Creating a Screening Design to Fit All Two-Factor Interactions
16(2)
A Compromise Design Between Main Effects Only and All Interactions
18(1)
Creating `Super' Screening Designs
19(4)
Screening Designs with Flexible Block Sizes
23(3)
Checking for Curvature Using One Extra Run
26(3)
Creating Response Surface Experiments
29(9)
Exploring the Prediction Variance Surface
29(3)
Introducing I-Optimal Designs for Response Surface Modeling
32(1)
A Three-Factor Response Surface Design
33(1)
Response Surface with a Blocking Factor
34(4)
Creating Mixture Experiments
38(6)
Mixtures Having Nonmixture Factors
38(3)
Experiments that are Mixtures of Mixtures
41(3)
Special-Purpose Uses of the Custom Designer
44(7)
Designing Experiments with Fixed Covariate Factors
44(3)
Creating a Design with Two Hard-to-Change Factors: Split Plot
47(4)
Technical Discussion
51(2)
3 Building Custom Designs The Basic Steps
53(34)
Creating a Custom Design
55(12)
Enter Responses and Factors into the Custom Designer
55(4)
Describe the Model
59(1)
Select the Number of Runs
60(1)
Understanding Design Evaluation
60(6)
Specify Output Options
66(1)
Make the JMP Design Table
67(1)
Creating Random Block Designs
67(1)
Creating Split Plot Designs
68(1)
Creating Split-Split Plot Designs
69(1)
Creating Strip Plot Designs
70(1)
Special Custom Design Commands
70(10)
Save Responses and Save Factors
71(1)
Load Responses and Load Factors
72(1)
Save Constraints and Load Constraints
72(1)
Set Random Seed: Setting the Number Generator
73(1)
Simulate Responses
73(1)
Save X Matrix: Viewing the Number of Rows in the Moments Matrix and the Design Matrix (X) in the Log
74(1)
Optimality Criterion: Changing the Design Criterion (D- or I- Optimality)
75(1)
Number of Starts: Changing the Number of Random Starts
76(1)
Sphere Radius: Constraining a Design to a Hypersphere
77(1)
Disallowed Combinations: Accounting for Factor Level Restrictions
78(1)
Advanced Options for the Custom Designer
79(1)
Save Script to Script Window
80(1)
Assigning Column Properties
80(5)
Define Low and High Values (DOE Coding) for Columns
81(1)
Set Columns as Factors for Mixture Experiments
81(2)
Define Response Column Values
83(1)
Assign Columns a Design Role
83(1)
Identify Factor Changes Column Property
84(1)
How Custom Designs Work: Behind the Scenes
85(2)
4 Screening Designs
87(26)
Screening Design Examples
89(6)
Using Two Continuous Factors and One Categorical Factor
89(2)
Using Five Continuous Factors
91(4)
Creating a Screening Design
95(10)
Enter Responses
95(1)
Enter Factors
96(1)
Choose a Design
97(3)
Display and Modify a Design
100(3)
Specify Output Options
103(1)
View the Design Table
104(1)
Create a Plackett-Burman design
105(1)
Analysis of Screening Data
106(7)
Using the Screening Analysis Platform
107(1)
Using the Fit Model Platform
108(5)
5 Response Surface Designs
113(14)
A Box-Behnken Design: The Tennis Ball Example
115(6)
The Prediction Profiler
117(2)
A Response Surface Plot (Contour Profiler)
119(1)
Geometry of a Box-Behnken Design
120(1)
Creating a Response Surface Design
121(6)
Enter Responses and Factors
122(1)
Choose a Design
122(2)
Specify Output Options
124(1)
View the Design Table
124(3)
6 Full Factorial Designs
127(10)
The Five-Factor Reactor Example
129(5)
Analyze the Reactor Data
130(4)
Creating a Factorial Design
134(3)
Enter Responses and Factors
134(1)
Select Output Options
135(1)
Make the Table
136(1)
7 Mixture Designs
137(24)
Mixture Design Types
139(1)
The Optimal Mixture Design
139(1)
The Simplex Centroid Design
140(3)
Creating the Design
140(1)
Simplex Centroid Design Examples
141(2)
The Simplex Lattice Design
143(2)
The Extreme Vertices Design
145(4)
Creating the Design
145(1)
An Extreme Vertices Example with Range Constraints
146(2)
An Extreme Vertices Example with Linear Constraints
148(1)
Extreme Vertices Method: How It Works
149(1)
The ABCD Design
149(1)
Creating Ternary Plots
150(1)
Fitting Mixture Designs
151(2)
Whole Model Tests and Analysis of Variance Reports
152(1)
Understanding Response Surface Reports
153(1)
A Chemical Mixture Example
153(8)
Create the Design
153(2)
Analyze the Mixture Model
155(1)
The Prediction Profiler
156(2)
The Mixture Profiler
158(1)
A Ternary Plot of the Mixture Response Surface
158(3)
8 Discrete Choice Designs
161(16)
Introduction
163(1)
Create an Example Choice Experiment
164(3)
Analyze the Example Choice Experiment
167(2)
Design a Choice Experiment Using Prior Information
169(2)
Administer the Survey and Analyze Results
171(6)
Initial Choice Platform Analysis
172(1)
Find Unit Cost and Trade Off Costs with the Profiler
173(4)
9 Space-Filling Designs
177(20)
Introduction to Space-Filling Designs
179(1)
Sphere-Packing Designs
179(3)
Creating a Sphere-Packing Design
179(2)
Visualizing the Sphere-Packing Design
181(1)
Latin Hypercube Designs
182(2)
Creating a Latin Hypercube Design
182(1)
Visualizing the Latin Hypercube Design
183(1)
Uniform Designs
184(1)
Comparing Sphere-Packing, Latin Hypercube, and Uniform Methods
185(2)
Minimum Potential Designs
187(1)
Maximum Entropy Designs
188(2)
Gaussian Process IMSE Optimal Designs
190(1)
Borehole Model: A Sphere-Packing Example
190(7)
Create the Sphere-Packing Design for the Borehole Data
191(1)
Guidelines for the Analysis of Deterministic Data
192(1)
Results of the Borehole Experiment
193(4)
10 Nonlinear Designs
197(14)
Examples of Nonlinear Designs
199(8)
Using Nonlinear Fit to Find Prior Parameter Estimates
199(4)
Creating a Nonlinear Design with No Prior Data
203(4)
Creating a Nonlinear Design
207(2)
Identify the Response and Factor Column with Formula
207(1)
Set Up Factors and Parameters in the Nonlinear Design Dialog
207(1)
Enter the Number of Runs and Preview the Design
208(1)
Make Table or Augment the Table
209(1)
Advanced Options for the Nonlinear Designer
209(2)
11 Taguchi Designs
211(10)
The Taguchi Design Approach
213(1)
Taguchi Design Example
213(4)
Analyze the Data
215(2)
Creating a Taguchi Design
217(4)
Detail the Response and Add Factors
217(1)
Choose Inner and Outer Array Designs
218(1)
Display Coded Design
219(1)
Make the Design Table
219(2)
12 Augmented Designs
221(22)
A D-Optimal Augmentation of the Reactor Example
223(8)
Analyze the Augmented Design
225(6)
Creating an Augmented Design
231(8)
Replicate a Design
231(2)
Add Center Points
233(1)
Creating a Foldover Design
234(1)
Adding Axial Points
235(1)
Adding New Runs and Terms
236(3)
Special Augment Design Commands
239(2)
Save the Design (X) Matrix
240(1)
Modify the Design Criterion (D- or I- Optimality)
240(1)
Select the Number of Random Starts
241(1)
Specify the Sphere Radius Value
241(1)
Disallow Factor Combinations
241(2)
13 Prospective Sample Size and Power
243(24)
Launching the Sample Size and Power Platform
245(1)
One-Sample and Two-Sample Means
245(6)
Single-Sample Mean
247(2)
Sample Size and Power Animation for One Mean
249(1)
Two-Sample Means
250(1)
k-Sample Means
251(1)
One Sample Standard Deviation
252(2)
One Sample Standard Deviation Example
253(1)
One-Sample and Two-Sample Proportions
254(5)
One Sample Proportion
254(2)
Two Sample Proportions
256(3)
Counts per Unit
259(1)
Counts per Unit Example
260(1)
Sigma Quality Level
260(7)
Sigma Quality Level Example
261(1)
Number of Defects Computation Example
261(6)
Index Design of Experiments 267