# 68 x 065 x 227r 245r versus fits response is year

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Unformatted text preview: NGE OF X IS FROM 2256 to 7018 we cannot predict for universities charging outside that range nalysis of Variance ource egression esidual Error otal DF 1 31 32 SS 143190926 39866422 183057349 MS 143190926 1286014 F 111.34 P 0.000 nusual Observations bs 1 2 7 10 Year 2000 7018 7002 4994 4715 Year 2008 13706 13642 12106 6385 Fit 13010 12983 9584 9112 SE Fit 494 491 235 214 Residual 696 659 2522 -2727 St Resid 0.68 X 0.65 X 2.27R -2.45R Versus Fits (response is Year 2008) 3000 2000 Residual 1000 0 -1000 -2000 -3000 4000 5000 6000 7000 8000 9000 10000 Fitted Value 11000 12000 13000 RESIDUALS: no curves, no wedges MEANING: errors are about the same for any values of X we decide to use R‐SQUARED: redictor onstant ear 2000 Coef 1132.8 1.6924 = 1134.03 SE Coef 701.4 0.1604 R-Sq = 78.2% T 1.61 10.55 P 0.116 0.000 R-Sq(adj) = 77.5% MAXIMUM ERROR : 2*1134.03, about \$2,268 THAT IS THE MODEL’S MAX ERROR PREDICT A TUITION VALUE: if in 2000 University XWY, charged \$5,000, how much are they likely to charge (average of all those who charged \$5,000) in 2008? A REGRESSION MODEL PREDICTS BETTER WHEN THE “X” VALUE IS NEAR THE AVERAGE OF ALL X’s because of that WE WISH WE COULD HAVE A VERY SMALL STANDARD ERROR (there are ways to DECREASE THE ERROR, AN EASY ONE IS ELIMINATE AN OUTLIER). DEC 5TH: ANOVA (CHAPTER 12) ECEMBER 5TH, LAST CLASS: ANOVA. CENARIO: OU WANT TO FIND DIFFERENCES IN DOSAGES (means). THE...
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