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Applied Data Analysis for Process Improvement A Practical Guide to Six Sigma Black Belt Statistics 1st Edition by James L Lamprecht ISBN 0873896483 9780873896481

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Applied data analysis for process improvement a practical guide to six sigma black belt statistics 1st Edition Hytinen Digital Instant Download

Author(s): Hytinen, Annemieke; Lamprecht, James L.; O’Mara, Paul
ISBN(s): 9780873896481, 0873896483
Edition: 1
File Details: PDF, 1.57 MB
Year: 2005
Language: english
SKU: EB-5307964 Category: Tags: , , , , ,

Applied Data Analysis for Process Improvement A Practical Guide to Six Sigma Black Belt Statistics 1st Edition by James L Lamprecht – Ebook PDF Instant Download/Delivery: 0873896483, 9780873896481
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ISBN 10: 0873896483 
ISBN 13: 9780873896481
Author: James L Lamprecht

At last, a book that offers the reader a practical approach to process improvement using examples of common problems faced by data analysts! Author James L. Lamprecht, an experienced, widely published statistician, Master Black Belt, teacher, and consultant, has succeeded in combining examples that guide the reader through data analysis, Six Sigma project definition, conducting experiments, graphical analysis, and errors to avoid, all in one concise text. Unlike other books on data analysis, Lamprecht steers clear of classic, or “perfect” examples, preferring instead to address the everyday issues that data analysts confront, and explain the value certain data does and does not offer. The book includes numerous graphs that illustrate ways to intuitively analyze data. Data analysis techniques are presented first, then the author introduces Six Sigma concepts and integrates the two disciplines in a concluding chapter.!–nl–This book is ideal for Certified Six Sigma Black Belts as well as those who are uncertified, but would like to understand how data can be analyzed. Even those who rely on sophisticated statistical software to conduct their Six Sigma analysis will benefit from this insightful yet easy-to-use book by developing a true understanding of statistics and a better understanding of the results they are receiving. Numerous examples illustrate how various techniques are applied. Each example is reviewed from the perspective of what was not said in the example; in other words, the very information you will be faced with when you conduct your own analysis. Titles of some sections in the book include the words “optional” or “advanced.” These sections cover more advanced but nonetheless useful topics, but skipping these sections will not affect the overall flow of the various subjects presented.

Applied Data Analysis for Process Improvement A Practical Guide to Six Sigma Black Belt Statistics 1st Table of contents:

PART I Applied Data Analysis
1 Intuitive Statistics: The Values of Graphs for Decision Making
1.0 INTRODUCTION
1.1 THE USE OF GRAPHS
1.2 OTHER GRAPHING TECHNIQUES: THE BOX PLOT DIAGRAM
1.3 STANDARD DEVIATION AND STANDARD ERROR (OF THE MEAN)
1.4 EXPERIMENTAL ASSUMPTIONS
1.5 HOW TO COLLECT DATA
1.6 MEASUREMENT AND ENVIRONMENTAL CONDITIONS AS SOURCES OF ERROR
1.7 CONCLUSION
2 Frequency Distributions
2.0 HISTOGRAMS AND THE NORMAL DISTRIBUTION
2.1 PROPERTIES OF THE NORMAL DISTRIBUTION
2.2 FREQUENCY DISTRIBUTION OF AVERAGES
2.3 DEGREES OF FREEDOM
2.4 CHI-SQUARE (χ2) AND F DISTRIBUTIONS
2.5 CONCLUSION
3 Statistical Inference
3.0 INTRODUCTION
3.1 POPULATION VERSUS SAMPLE
3.2 POINT ESTIMATION
3.3 HYPOTHESIS TESTING
3.4 HOW TO TEST A HYPOTHESIS: STATISTICAL INFERENCE
3.5 TESTS CONCERNING MEANS (Z-TEST FOR SAMPLE SIZE > 30)
3.6 TEST CONCERNING DIFFERENCES BETWEEN MEANS (Z-TEST ORT-TEST AND PAIRED T-TEST)
3.7 TESTS CONCERNING VARIANCES (F-TEST)
3.8 TESTS BASED ON COUNT DATA
3.9 HOW TO DETERMINE SAMPLE SIZE
3.10 CONCLUSION
4 Design of Experiments
4.0 INTRODUCTION
4.1 ONE-WAY ANOVA
4.2 TWO-WAY ANOVA
4.3 HOW ABOUT INTERACTIONS?
4.4 COMMENTS REGARDING THE SUM OF SQUARES FOR ERROR (SSE)
4.5 EXAMPLE 4.4 LATIN SQUARE (ADVANCEDSECTION: REFER TO APPENDIX A)
4.6 WHAT IS THE MEANING OF THE WORD FIXED APPENDED TO EACH FACTOR IN TABLE 4.13?
4.7 ADVANCED TOPIC
4.8 CONCLUSION
5 Factorial Designs and Fractional Factorial Designs
5.0 INTRODUCTION
5.1 FACTORIAL DESIGNS AT TWO LEVELS
5.2 THE USE OF REPLICATIONTO ESTIMATE ERROR TERMS
5.3 FRACTIONAL FACTORIAL
5.4 NONMANUFACTURING EXAMPLE
5.5 WHAT ABOUT TWO OR MORE RESPONSE VARIABLES? AN EXAMPLE FROM RESEARCH DESIGN
5.6 ECONOMIC CONSIDERATIONS
5.7 CONCLUSION: HOW TOSET UP AN EXPERIMENT
6 Regression Analysis
6.0 INTRODUCTION
6.1 RELATIONSHIP BETWEEN TWO VARIABLES (CORRELATION COEFFICIENT)
6.2 LINEAR REGRESSION ANALYSIS: ANEXAMPLE
6.3 CURVILINEAR REGRESSIONS
6.4 USING DUMMY VARIABLES
6.5 REGRESSION MODELS WITH AND WITHOUT INTERACTIONS
6.6 CONCLUSION: MODEL BUILDING WITH REGRESSION EQUATIONS
7 Response Surfaces
7.0 INTRODUCTION
7.1 RESPONSE SURFACE DESIGNS: BOX-BEHNKEN
7.2 CENTRAL COMPOSITE DESIGN
7.3 FINDING THE OPTIMUM SETTING TO CENTER A PROCESS AND MINIMIZE THE STANDARD DEVIATION
7.4 POTENTIAL PROBLEMS TO AVOID WHEN RUNNING A DoE
7.5 CONCLUSION
Part II The DMAIC Methodology
8 On Problem Definition
8.0 INTRODUCTION
8.1 THE DMAIC MODEL: THE FOUNDATION OF SIX SIGMA
8.2 DATA: THE SOURCE FOR MOST PROBLEM DEFINITIONS
8.3 TYPES OF PROBLEM OPPORTUNITIES
8.4 TYPE I: INTERNAL EFFICIENCY OPPORTUNITIES
8.5 THE QUALITATIVE APPROACH
8.6 ANALYZING THE RESULT: THE AFFINITY DIAGRAM
8.7 PROBLEM DEFINITION: THE KEY TO A SUCCESSFUL PROJECT
8.8 WHAT TO CONSIDER WHEN SELECTING A PROJECT
8.9 PROCESS ANALYSIS PHASE (ANALYZE)
8.10 THE CAUSE-AND-EFFECT MATRIX
8.11 GENERIC TYPES OF PROBLEMS AND ASSOCIATED METHODOLOGY
8.12 THE DEFINE-MEASURE-ANALYZE INTERPHASE
8.13 IMPROVE
8.14 VERIFY: PILOT RUN
8.15 CONTROL: THE LAST PHASE
8.16 CONCLUSION: INVESTIGATING THE DATA
9 Case Study
9.0 BACKGROUND INFORMATION
9.1 HOW SHOULD THE INVESTIGATOR PROCEED?
9.2 WHAT DOES THE CONTROL CHART SHOW US?
9.3 SOME LESSONS LEARNED

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James L Lamprecht,Data Analysis,Six Sigma,Belt Statistics