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Unformatted text preview: 97 and 102 of the Chapter 6 (SLR). > dataslr < read.table("slr.data.txt",header=TRUE) > > mylm< lm(Y X, data=dataslr) > summary(mylm) Call: lm(formula = Y X, data = dataslr) Residuals: 1 2 3 4 5 1.0804 1.6964 0.9554 0.7946 2.3661 Coefficients: Estimate Std. Error t value Pr(>t) (Intercept) 8.4018 2.6434 3.178 0.0502 . 1 X 0.4196 0.1542 2.721 0.0725 .Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 1.931 on 3 degrees of freedom Multiple Rsquared: 0.7117, Adjusted Rsquared: 0.6155 Fstatistic: 7.404 on 1 and 3 DF, pvalue: 0.07248 > > > anova(mylm) Analysis of Variance Table Response: Y Df Sum Sq Mean Sq F value Pr(>F) X 1 27.613 27.6125 7.4045 0.07248 . Residuals 3 11.188 3.7292Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 > 2...
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 Summer '08
 TA
 Statistics, Linear Regression

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