Lec 02 - Regression

Lec 02 - Regression - -3.238 -0.314 0.949 -2.925 -2.24R 2...

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Regression Data Y X Residual: -4.23813 1 -3.23813 0.36827 2 2.36827 -0.46647 3 2.53353 -0.38381 4 3.61619 -0.23722 5 4.76278 -1.53395 6 4.46605 1.52628 7 8.52628 -1.07464 8 6.92536 -2.15809 9 6.84191 -0.70236 10 9.29764 X Y 10 8 6 4 2 0 10.0 7.5 5.0 2.5 0.0 -2.5 -5.0 Scatterplot of Y vs X
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Regression Analysis: Y versus X The regression equation is Y = - 1.41 + 1.09 X Predictor Coef SE Coef T P Constant -1.408 1.103 -1.28 0.238 X 1.0941 0.1778 6.16 0.000 S = 1.61456 R-Sq = 82.6% R-Sq(adj) = 80.4% Analysis of Variance Source DF SS MS F P Regression 1 98.761 98.761 37.89 0.000 Residual Error 8 20.854 2.607 Total 9 119.615 Obs X Y Fit SE Fit Residual St Resid 1 1.0
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Unformatted text preview: -3.238 -0.314 0.949 -2.925 -2.24R 2 2.0 2.368 0.781 0.805 1.588 1.13 3 3.0 2.534 1.875 0.677 0.659 0.45 4 4.0 3.616 2.969 0.576 0.647 0.43 5 5.0 4.763 4.063 0.518 0.700 0.46 6 6.0 4.466 5.157 0.518 -0.691 -0.45 Residual Percent 4 3 2 1-1-2-3-4 99 95 90 80 70 60 50 40 30 20 10 5 1 Normal Probability Plot of the Residuals (response is Y) 7 7.0 8.526 6.251 0.576 2.275 1.51 8 8.0 6.925 7.345 0.677 -0.420 -0.29 9 9.0 6.842 8.439 0.805 -1.598 -1.14 10 10.0 9.298 9.534 0.949 -0.236 -0.18 R denotes an observation with a large standardized residual....
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This note was uploaded on 09/20/2011 for the course ENG 300 taught by Professor Albin during the Fall '11 term at Rutgers.

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Lec 02 - Regression - -3.238 -0.314 0.949 -2.925 -2.24R 2...

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