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Tables of Contents for Statistical Thinking
Chapter/Section Title
Page #
Page Count
PART I Statistical Thinking Concepts
1
92
The Need for Business Improvement
3
20
Overview
3
1
Today's Business Realities and the Need to Improve
3
4
We Now Have Two Jobs: A Model for Business Improvement
7
3
New Management Approaches Require Statistical Thinking
10
4
Principles of Statistical Thinking
14
4
Applications of Statistical Thinking
18
1
Summary
19
4
Exercises
19
2
References
21
2
The Overall Statistical Thinking Approach
23
32
Overview
23
1
Case Study: The Effect of Advertising on Sales
24
5
The First Experiment
24
1
The Second Experiment
25
1
Refining the Research Hypothesis
26
1
Research Outcomes
27
2
Summary
29
1
Case Study: Improvement of a Soccer Team's Performance
29
10
Background
29
1
Overall Approach
29
1
Getting Started
30
1
First Round of Data Collection
31
2
Second and Third Sets of Defensive Data
33
3
Offensive Skills
36
1
Next Round on Offense
37
2
Summary
39
1
A Model for Statistical Thinking
39
5
The Commonality of Approach
39
2
Statistical Thinking Model
41
1
Relationship to the Scientific Method and the PDCA Cycle
42
2
Variation in Business Processes
44
4
The Synergy Between Data and Subject Matter Knowledge
48
1
The Dynamic Nature of Business Processes
49
1
Summary
50
1
Project Update
51
4
Exercises
52
2
References
54
1
Understanding Business Processes
55
38
Overview
55
1
Examples of Business Processes
56
5
The SIPOC Model for Processes
61
3
Identifying Business Processes
64
1
Analysis of Business Processes
65
14
Non-Value-Added Work
65
4
Process Complexity
69
3
The Hidden Plant
72
2
Process Measurements
74
4
Benchmarking
78
1
Systems of Processes
79
3
The Measurement Process
82
5
Summary
87
1
Project Update
88
5
Exercises
88
3
References
91
2
PART II Improvement Strategies and Basic Tools
93
124
Process Improvement and Problem-Solving Strategies
95
45
Overview
95
1
Case Study: Reducing Resin Output Variation
96
6
Case Study: Reducing Telephone Waiting Time at a Bank
102
4
The Process Improvement Strategy
106
5
Case Study: Resolving Customer Complaints of Baby Wipe Flushability
111
7
Case Study: The Realized Revenue Fiasco
118
5
The Problem-Solving Strategy
123
4
The Six Sigma Process Improvement Strategy
127
3
Summary
130
1
Project Update
130
4
Exercises
130
3
References
133
1
Introduction to Microsoft Excel
134
6
Overview
134
1
What Is Excel?
134
1
Data Storage
135
1
Calculations
136
1
Graphics in Excel
137
1
Data Analysis
138
1
Summary
139
1
References
139
1
Process Improvement and Problem-Solving Tools
140
77
Introduction
140
1
Relationship of the Tools of the Strategies
141
1
Data Collection Tools
141
12
Checksheet
141
4
Data Sheet
145
1
Surveys
145
3
Practical Sampling Tips
148
5
Data Analysis Tools
153
32
Box Plots
153
3
Capability Analysis
156
4
Control Charts
160
13
Histogram
173
3
Pareto Chart
176
2
Run Chart (Time Plot)
178
2
Scatter Plot
180
4
Stratification
184
1
Knowledge-Based Tools
185
22
Affinity Diagram
185
5
Brainstorming
190
1
Cause-and-Effect Diagram
191
3
Five Whys
194
1
Flowchart
195
4
Interrelationship Diagraph
199
4
Is-Is Not Analysis
203
2
Multivoting
205
2
Summary
207
1
Project Update
207
10
Exercises
207
8
References
215
2
PART III Formal Statistical Methods
217
240
Introduction to Minitab
219
5
Overview
219
1
What Is Minitab?
219
1
Data Storage
220
1
Statistical Calculations and Graphs
221
2
Summary
223
1
Introduction to JMP
224
6
Overview
224
1
What Is JMP?
224
1
Data Storage
225
1
Statistical Calculations and Graphs
226
2
JMP Tools
228
1
Summary
229
1
Building and Using Models
230
54
Overview
230
1
Examples of Business Models
231
3
Types and Uses of Models
234
2
Uses of Models
235
1
The Regression Modeling Process
236
7
Multiple Predictor Variables
237
1
A Method for Building Regression Models
238
1
Least Squares
238
5
Building Models with One Predictor Variable
243
9
Get to Know Your Data
244
2
Formulate the Model
246
1
Fit the Model to the Data
246
1
Check the Model Fit
246
5
Report and Use the Model
251
1
Extrapolation Can Be Like Skating on Thin Ice
252
1
Building Models with Several Predictor Variables
252
7
Get to Know Your Data
254
3
Formulate the Model
257
1
Fit the Model to the Data
257
1
Check the Model Fit
258
1
Report and Use the Model
259
1
Multicollinearity, Another Model Check
259
3
Some Limitations of Using Existing Data
262
1
Summary
263
2
Project Update
265
19
Exercises
265
17
References
282
2
Using Process Experimentation to Build Models
284
43
Overview
284
1
Why Do We Need a Statistical Approach?
285
2
Haphazard Experimentation
285
1
One-Factor-at-a-Time Experimentation
285
1
The Statistical Approach
286
1
Examples of Process Experiments
287
6
The Effect of Advertising on Sales
287
1
Product Development Case Study
288
3
Reducing Defects in Plastic Parts Case Study
291
2
The Statistical Approach to Experimentation
293
7
Planning Test Programs
296
1
Designing the Experiment
297
3
Two-Factor Experiments: A Case Study
300
6
Interaction Between Factors
303
1
Regression Analysis of Two-Level Designs
304
2
Three-Factor Experiments: A Case Study
306
6
Designing the Experiment
306
1
Analysis of Results
306
5
Importance of the Factor Effects
311
1
Efficiency and Hidden Replication
311
1
Larger Experiments
312
1
Blocking, Randomization, and Center Points
313
2
Summary
315
1
Project Update
316
11
Exercises
317
9
References
326
1
Applications of Statistical Inference Tools
327
47
Overview
327
2
Examples of Statistical Inference Tools
329
3
The Process of Applying Statistical Inference
332
4
Statistical Confidence and Prediction Intervals
336
11
Confidence Interval for the Average
337
2
Prediction Interval for One Observation
339
1
Confidence Interval for the Proportion
340
1
Confidence Interval for the Standard Deviation
341
2
Confidence Interval for a Regression Coefficient
343
1
Prediction Interval for Future y Values Using a Regression Equation
344
1
Confidence Interval for the Difference Between Two Averages
345
1
Confidence Interval for the Difference Between Two Proportions
346
1
Statistical Hypothesis Tests
347
8
The Hypothesis Testing Process
347
3
Connection to Confidence Intervals
350
1
Stating the Hypotheses
351
1
Obtaining the Data
352
1
Evaluating the Consistency Between the Data and the Null Hypothesis
352
2
Rejecting or Failing to Reject
354
1
Tests for Continuous Data
355
4
Test for One Average
355
1
Test for Comparing Two Averages
356
1
Test for Comparing Several Averages
357
1
Test for Comparing Two Variances (Standard Deviations)
358
1
Test for Comparing Several Variances (Standard Deviations)
358
1
Test for Discrete Data
359
1
Test for Comparing Two or More Proportions
359
1
Test for Regression Analysis
360
1
Test on a Regression Coefficient
360
1
Sample Size Formulas
361
5
Sampling from an Infinite Population
361
3
Sampling from Finite Populations
364
1
Sample Sizes for Hypothesis Tests
365
1
Summary
366
1
Project Update
366
8
Exercises
367
6
References
373
1
The Underlying Theory of Statistical Inference
374
59
Overview
374
1
Applications of the Theory
375
2
The Theoretical Framework of Statistical Inference
377
4
Types of Data
381
3
Nominal Data
381
1
Ordinal Data
382
1
Integer Data
382
1
Continuous Data
383
1
Probability Distributions
384
15
Discrete Distributions
385
6
Continuous Distributions
391
8
Sampling Distributions
399
7
The Sample Average
399
1
The Central Limit Theorem
400
3
The Sample Variance (Standard Deviation)
403
1
The t Distribution
404
2
Linear Combinations
406
2
Transformations
408
20
Why Do We Use Transformations?
408
3
Other Examples of Transformations
411
3
The Goodwill Case
414
10
The Process of Applying Transformations
424
4
Summary
428
1
Project Update
428
5
Exercises
429
3
References
432
1
Summary and Path Forward
433
24
Overview
433
1
A Personal Case Study
434
6
Tom Pohlen
The Objectives
434
1
The ``Process''
434
1
The Goal
435
1
Understanding Variation
435
1
Finding Solutions
436
1
Successful Results
436
1
Benefits
437
2
Lessons Learned
439
1
Case Study: Newspaper Accuracy
440
7
Introduction
440
1
Project Definition
441
1
Process Measurement
442
1
Process Analysis
443
2
Process Improvement
445
1
Process Control
446
1
Results
447
1
Review of the Statistical Thinking Approach
447
3
Text Summary
450
2
Potential Next Steps to Deeper Understanding of Statistical Thinking
452
1
Project Summary and Debriefing
453
4
Exercises
454
1
References
454
3
Appendix A Effective Teamwork
457
8
Appendix B Presentations and Report Writing
465
4
Appendix C More on Surveys
469
7
Appendix D More on the Six Sigma Improvement Approach
476
7
Appendix E More on Design of Experiments
483
13
Appendix F More on Inference Tools
496
3
Appendix G More on Probability Distributions
499
6
Appendix H Process Design (Reengineering)
505
5
Appendix I t Critical Values
510
2
Appendix J Standard Normal Probabilities (Cumulative z Curve Areas)
512
3
Index
515