Statistical Thinking

by Hoerl, Roger W.; Snee, Ronald D.

• ISBN13: 9780534381585
• ISBN10: 0534381588
• Format: Hardcover
• Copyright: 2001-02-01
• Publisher: Cengage Learning

Summary

This innovative book teaches students to understand the strategic value of data and statistics in solving real business problems. Following principles of effective learning identified by educational and behavioral research, the instruction proceeds from tangible examples to abstract theory; from the big picture, or whole, to details, or parts; and from a conceptual understanding to ability to perform specific tasks. While the computer is used for computational details, the authors describe the role of statistical thinking and methods for problem solving and process improvement to encourage use of the tools. Hoerl and Snee also teach skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, graphically analyzing data using basic tools, deriving actionable conclusions from data analyses, and understanding the limitations of statistical analyses. In summary, the authors demonstrate that statistical thinking and methodology can help students be more valuable and effective in their chosen careers.

Table of Contents

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