SLRegExample - -12.212 -2.13R 13 48 57.500 66.281 0.699...

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110 100 90 80 70 60 50 40 30 110 100 90 80 70 60 50 40 30 Exam1 Grade Scatterplot of Grade vs Exam1 Example: Can we predict your final grade in the class from your 1 st exam score? Regression Analysis: Grade versus Exam1 The regression equation is Grade = 36.8 + 0.614 Exam1 Predictor Coef SE Coef T P Constant 36.832 1.655 22.26 0.000 Exam1 0.61352 0.02060 29.78 0.000 S = 5.76575 R-Sq=67.1% R-sq(adj)=67.1% Analysis of Variance Source DF SS MS F P Regression 1 29480 29480 886.77 0.000 Residual Error 434 14428 33 Total 435 43908 Unusual Observations Obs Exam1 Grade Fit SE Fit Residual St Resid 1 72 57.250 81.005 0.313 -23.755 -4.13R 2 42 58.000 62.600 0.814 -4.600 -0.81 X 4 72 60.313 81.005 0.313 -20.693 -3.59R 5 51 34.813 68.121 0.643 -33.309 -5.81R 6 36 53.720 58.919 0.932 -5.199 -0.91 X 7 63 60.000 75.484 0.433 -15.484 -2.69R 10 54 57.750 69.962 0.588
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Unformatted text preview: -12.212 -2.13R 13 48 57.500 66.281 0.699 -8.781 -1.53 X 15 75 67.500 82.846 0.289 -15.346 -2.66R 24 81 69.000 86.527 0.279 -17.527 -3.04R 33 45 65.250 64.440 0.756 0.810 0.14 X 39 78 72.750 84.686 0.277 -11.936 -2.07R Etc. R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Predicted Values for New Observations New Obs Exam1 Fit SE Fit 95% CI 95% PI 1 80.0 85.913 0.277 (85.370, 86.457) (74.568, 97.259) 20-20-40 99.9 99 90 50 10 1 0.1 Residual Percent 100 90 80 70 60 20-20-40 Fitted Value Residual 22.5 15.0 7.5 0.0-7.5-15.0-22.5-30.0 100 75 50 25 Residual Frequency 400 350 300 250 200 150 100 50 1 20-20-40 Observation Order Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Grade...
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This note was uploaded on 12/15/2011 for the course STA 3024 taught by Professor Ta during the Spring '08 term at University of Florida.

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