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by: Richie Hauck

StatisticalMethodsforTesting,DevelopmentandManufacturingI ISE301

Marketplace > Wright State University > Industrial Engineering > ISE301 > StatisticalMethodsforTesting DevelopmentandManufacturingI
Richie Hauck
GPA 3.83


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Class Notes
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This 1 page Class Notes was uploaded by Richie Hauck on Thursday October 29, 2015. The Class Notes belongs to ISE301 at Wright State University taught by DavidKender in Fall. Since its upload, it has received 8 views. For similar materials see /class/231120/ise301-wright-state-university in Industrial Engineering at Wright State University.

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Date Created: 10/29/15
Simple Linear Correlation Simple Two Variables X amp Y Linear Straight Line Y mX b Correlation Mathematical Relationship Assumptions 1 Both X and Y are random variables 2 Data pairs X Y are Bivariate Normal Distributions For any xed value of X the Y values are normally distributed For any xed value of Y the X values are normally distributed Properties of r l The value of r is a measure oflinear relationship only 2 l lt r lt 1 For r 1 perfect negative correlation For r 1 perfect positive correlation If r 0 then zero linear correlation Common Errors Signi cant linear correlation does NOT provide proof of Cause amp Effect N Lack of signi cant LINEAR correlation does not imply there is no other mathematical relationship U Tests for correlation should not based on rates or averages 1 Do not use the regression equation for predicting if there is no signi cant correlation kJI When using the regression equation for predicting stay Within the range of the X variable 0 A regression equation based on old data is not necessarily valid for current situations 1 A regression equation based on current data is not necessarily valid for future situations 00 Do not use the regression equation to make predictions about a population that is different from the population from Which the sample data were drawn


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