Statistics Roundtable
2001
My process is too variable--now what do I do?
How to produce and use a successful multi-vari study
How to produce and use a successful multi- vari study by Ronald D. Snee Process Schematic FIGURE 1 The process Process outputs Controlled variables Customer Process inputs Uncontrolled noise variables Manufacturing Process Variables TABLE 1 Process input...
Inferences on percentage changes.
A popular approach to the problem of inferences on functions of averages
Q U A L I T Y P R O G R E S S I O C T O B E R 2 0 0 1 I 57 Turbine Engine Average Deposits TABLE 2 Changes from lubricant eight p- values Standard Percentage Percentage Lubricant Average deviation Difference change Difference change 1 24.89 20.11 - 26.61...
Column: Statistics Roundtable: Chicken Soup for Processes
Understanding process variation is a prerequisite to using SPC
SPC can help hold onto gains once they are attained by being used for Shewhart's originally intended purpose -- to detect the entry of assignable cause variation....
Column: Statistics Roundtable: Using degradation data for product liability analysis.
A case study shows how this type of data can provide more precise results in assessing reliability
High reliability systems require individual components to have extremely high reliability for a long time. Often, the time for product development is short, imposing severe constraints on reliability testing. Traditionally, methods for the analysis of...
Statistics Roundtable: Implementing Multivariate SPC Using Hotelling's T2 Statistic
Multivariate statistical process control (MVSPC) can be defined as the application of multivariate statistical procedures for the purpose of increasing the quality and productivity of a business. Hotelling's T2 statistic allows you to monitor many...
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