Essential Statistical Concepts for the Quality Professional

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A01=D. H. Stamatis
advanced statistical tools for quality management
ANCOVA Mixed Model
ANCOVA Model
Anderson Darling Test
ANOVA Table
Author_D. H. Stamatis
Barnard's Test
Binomial Experiment
Category=KJMD
Category=KJMQ
Cluster Sampling
Con Dence Interval
condence
Condence Intervals
Cumulative Distribution Functions
data analysis strategies
deviation
Disproportionate Stratication
distribution
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
experimental design methods
hypothesis
interval
Kuiper's Test
Latin Square
Leaf Display
Linear Correlation Test
Multiple Comparison Procedures
Nonparametric Multiple Comparison Test
Null Hypothesis
Pearson's Chi Square Test
process improvement techniques
regression modeling applications
sample
sampling methodology
size
Skip Lot Sampling Plans
Split Plot Model
square
standard
Standardized Regression Coefcient
statistical quality control
Stratied Sampling
Unreplicated Design
Watson Williams Test

Product details

  • ISBN 9781439894576
  • Weight: 839g
  • Dimensions: 156 x 234mm
  • Publication Date: 02 May 2012
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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The essence of any root cause analysis in our modern quality thinking is to go beyond the actual problem. This means not only do we have to fix the problem at hand but we also have to identify why the failure occurred and what was the opportunity to apply the appropriate knowledge to avoid the problem in the future. Essential Statistical Concepts for the Quality Professional offers a new non-technical statistical approach to quality for effective improvement and productivity by focusing on very specific and fundamental methodologies and tools for the future.

Written by an expert with more than 30 years of experience in management, quality training, and consulting, the book examines the fundamentals of statistical understanding, and by doing so demonstrates the importance of using statistics in the decision making process. The author points out pitfalls to keep in mind when undertaking an experiment for improvement and explains how to use statistics in improvement endeavors. He discusses data interpretation, common tests and confidence intervals, and how to plan experiments for improvement. The book expands the notion of experimentation by dealing with mathematical models such as regression to optimize the improvement and understand the relationship between several factors. It emphasizes the need for sampling and introduces specific techniques to make sure accuracy and precision of the data is appropriate and applicable for the study at hand.

The author’s approach is somewhat new and unique; however, he details tools and methodologies that can be used to evaluate the system for prevention. These tools and methodologies focus on structured, repeatable processes that can be instrumental in finding real, fixable causes of the human errors and equipment failures that lead to quality issues.

Stamatis, D. H.

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