STAT 4220 HW2

STAT 4220 HW2 - Austin Wen Stat 4220 HW2 1) A. >

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Stat 4220 HW2 1) A. > g=lm(sqrt(Species)~Area+Elevation+Nearest+Scruz+Adjacent,data=data1) > summary(g) Call: lm(formula = sqrt(Species) ~ Area + Elevation + Nearest + Scruz + Adjacent, data = data1) Residuals: Min 1Q Median 3Q Max -4.5572 -1.4969 -0.3031 1.3527 5.2110 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.3919243 0.8712678 3.893 0.000690 *** Area -0.0019718 0.0010199 -1.933 0.065080 . Elevation 0.0164784 0.0024410 6.751 5.55e-07 *** Nearest 0.0249326 0.0479495 0.520 0.607844 Scruz -0.0134826 0.0097980 -1.376 0.181509 Adjacent -0.0033669 0.0008051 -4.182 0.000333 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.774 on 24 degrees of freedom Multiple R-squared: 0.7827, Adjusted R-squared: 0.7374 F-statistic: 17.29 on 5 and 24 DF, p-value: 2.874e-07 B. > plot(g$fit,g$res,xlab=expression(hat(y)),ylab="Residual",pch=4,col="blue",main=" Residual Plot") > abline(h=0) C. The pattern in the residual versus fitted plot is evenly spread across the x=0 line. D.
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This note was uploaded on 06/06/2011 for the course STAT 4220 taught by Professor Smith during the Spring '08 term at UGA.

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STAT 4220 HW2 - Austin Wen Stat 4220 HW2 1) A. >

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