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used to investigate a specific failure to find problem and root cause |
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x's are people, equipment, measurement, methods, environment, materials, and y is effect |
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used to see if relationship between two factors |
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used to measure strength of any correlation found, |
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+1 strong 0 no correlation, -1 strong inverse correlation |
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less than .05 then correlation exists, greater than .05 no correlation exists |
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devlping mathimatical model to represent ur data |
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performing simple regression, response y, predictor x |
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equation and what each means |
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y=b0+b1x, y=effect, x=cause, b1 slope, b0=yintercept |
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regression equation p value |
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greater .05 then input variable does influence process out, less .05 then it does not influence |
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indicated that input variables account for percentage of variation. <50% not strong |
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fitted line plot and check for strength of relationship, |
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check p values, check r2 for lurking variables, 4 in 1 residual plot, regression analysis |
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