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Tables of Contents for Introductory Statistics for Engineering Experimentation
Chapter/Section Title
Page #
Page Count
Preface
xi
 
1 Introduction
1
5
Variability
2
1
Experimental Design
2
1
Random Sampling
3
1
Randomization
3
1
Replication
4
1
Problems
5
1
2 Summarizing Data
6
29
2.1 Simple Graphical Techniques
6
12
Univariate Data
6
6
Bivariate Data
12
3
Problems
15
3
2.2 Numerical Summaries and Box Plots
18
8
Measures of Location
18
2
Measures of Spread
20
2
Box-and-Whisker Plots
22
3
Problems
25
1
2.3 Graphical Tools for Designed Experiments
26
7
Problems
31
2
2.4 Chapter Problems
33
2
3 Models for Experiment Outcomes
35
49
3.1 Models for Single-Factor Experiments
37
4
Parameter Estimation
37
3
Problems
40
1
3.2 Models for Two-Factor Factorial Experiments
41
8
A Model with No Interaction
42
2
A Model Accounting for Interaction
44
4
Problems
48
1
3.3 Models for Bivariate Data
49
18
Fitting Lines
49
5
Fitting Exponential Curves
54
7
Fitting Polynomial Curves
61
3
Problems
64
3
3.4 Models for Multivariate Data
67
4
Problems
69
2
3.5 Assessing the Fit of a Model
71
8
Coefficient of Determination
71
1
Residual Plots
72
5
Correlation Coefficient
77
1
Problems
78
1
3.6 Chapter Problems
79
5
4 Models for the Random Error
84
71
4.1 Random Variables
84
9
Density Functions and Probability Functions
85
1
The Mean and Variance of a Random Variable
85
2
Properties of Expected Values
87
1
Properties of Variances
88
1
Determining Probabilities
89
3
Problems
92
1
4.2 Important Discrete Distributions
93
13
The Bernoulli Distributions
93
1
The Binomial Distributions
93
3
The Poisson Distribution
96
4
The Geometric Distribution
100
3
Problems
103
3
4.3 Important Continuous Distributions
106
27
The Uniform Distribution
106
2
The Exponential Distribution
108
6
The Normal Distribution
114
8
The Lognormal Distribution
122
4
The Weibull Distribution
126
4
Problems
130
3
4.4 Assessing the Fit of a Distribution
133
17
Exponential Probability Plots
133
5
Normal Probability Plots
138
5
Lognormal Probability Plots
143
1
Weibull Probability Plots
144
4
Problems
148
2
4.5 Chapter Problems
150
5
5 Inference for a Single Population
155
62
5.1 Central Limit Theorem
155
10
Normal Approximation to the Binomial
158
2
Normal Approximation to the Poisson
160
2
Problems
162
3
5.2 A Confidence Interval for μ
165
13
Interpretation of the Confidence Coefficient
166
1
Large Samples
166
1
Small Samples
167
5
Sample Sizes
172
3
Problems
175
3
5.3 Prediction and Tolerance Intervals
178
12
Dependence on the Population Distribution
178
1
Intervals for Normal Populations
178
3
Intervals for Non-Normal Populations
181
7
Problems
188
2
5.4 Hypothesis Tests
190
13
Performing a Hypothesis Test
191
3
Sample Sizes
194
7
Problems
201
2
5.5 Inference for Binomial Populations
203
9
Confidence Intervals
203
2
Hypothesis Tests
205
4
Problems
209
3
5.6 Chapter Problems
212
5
6 Comparing Two Populations
217
28
6.1 Paired Samples
217
6
Problems
221
2
6.2 Independent Samples
223
9
Equal Variances
224
3
Unequal Variances
227
3
Problems
230
2
6.3 Comparing Two Binomial Populations
232
10
Confidence Intervals
232
2
Hypothesis Tests
234
6
Problems
240
2
6.4 Chapter Problems
242
3
7 One-Factor Multi-Sample Experiments
245
50
7.1 Basic Inference
246
4
Problems
249
1
7.2 The Analysis of Means
250
11
Problems
259
2
7.3 ANOM with Unequal Sample Sizes
261
5
Problems
265
1
7.4 ANOM for Proportions
266
7
Equal Sample Sizes
266
2
Unequal Sample Sizes
268
3
Problems
271
2
7.5 The Analysis of Variance
273
4
Equal Sample Sizes
273
1
Unequal Sample Sizes
274
2
Problems
276
1
7.6 The Equal Variances Assumption
277
10
Testing for Equal Variances
277
3
Transformation to Stabilize the Variance
280
6
Problems
286
1
7.7 Sample Sizes
287
3
Sample Sizes for ANOM
287
1
Sample Sizes for ANOVA
288
1
Problems
289
1
7.8 Chapter Problems
290
5
8 Experiments with Two Factors
295
43
8.1 Interaction
295
1
8.2 More Than One Observation Per Cell
296
26
Further Analysis When There in no AB Interaction
304
7
Further Analysis When There is an AB Interaction
311
7
Problems
318
4
8.3 Only One Observation per Cell
322
6
Problems
327
1
8.4 Blocking to Reduce Variability
328
6
Problems
332
2
8.5 Chapter Problems
334
4
9 Multi-Factor Experiments
338
31
9.1 ANOVA for Multi-Factor Experiments
338
12
Blocking Revisited
345
3
Problems
348
2
9.2 2k Factorial Designs
350
9
Yates' Algorithm
354
4
Problems
358
1
9.3 Fractional Factorial Designs
359
6
Generating Fractions of 2k Factorial Designs
359
2
Yates' Algorithm for Fractional Factorial Designs
361
3
Problems
364
1
9.4 Chapter Problems
365
4
10 Inference for Regression Models
369
26
10.1 Inference for a Regression Line
369
17
Inference for β1
370
5
Inference for E(Y|x)
375
3
Inference for Y
378
1
Planning an Experiment
379
1
Testing for Lack of Fit
379
4
Problems
383
3
10.2 Inference for Other Regression Models
386
5
Inference for the βi's
386
2
Inference for Y and E(Y|x)
388
1
Testing for Lack of Fit
388
2
Problems
390
1
10.3 Chapter Problems
391
4
11 Response Surface Methods
395
29
11.1 First-Order Designs
395
11
Reverse Yates' Algorithm
396
2
Center Points
398
4
Path of Steepest Ascent
402
3
Problems
405
1
11.2 Second-Order Designs
406
17
3k Factorial Designs
406
6
Central Composite Designs
412
10
Problems
422
1
11.3 Chapter Problems
423
1
12 Appendices
424
84
12.1 Appendix A - Descriptions of Data Sets
424
18
12.2 Appendix B - Tables
442
36
12.3 Appendix C - Figures
478
5
12.4 Appendix D - Sample Projects
483
25
13 References
508
3
Index
511