# Dat residuals min 1q 154995 70431 median 2069 3q

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Unformatted text preview: l: lm(formula = y ~ x1 + x2 + x1x2, data = clocks.dat) Residuals: Min 1Q -154.995 -70.431 Median 2.069 3Q 47.880 Max 202.259 Coefficients: Estimate Std. Error t value Pr(&gt;|t|) (Intercept) 320.4580 295.1413 1.086 0.28684 x1 0.8781 2.0322 0.432 0.66896 x2 -93.2648 29.8916 -3.120 0.00416 ** x1x2 1.2978 0.2123 6.112 1.35e-06 *** --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 88.91 on 28 degrees of freedom Multiple R-Squared: 0.9539, Adjusted R-squared: 0.9489 F-statistic: 193 on 3 and 28 DF, p-value: &lt; 2.2e-16 &gt; &gt; &gt; + + + &gt; &gt; gr = rep(1,32) for (i in 1:32) { if (x2[i]&gt;7) {gr[i]=gr[i]+1} if (x2[i]&gt;10) {gr[i]=gr[i]+1} } plot(x1,y,pch=gr,col=gr) An Interaction Model Relating Y to Two Quantitative Independent Variables Y = β0 + β1 x1 + β2 x2 + β3 x1 x2 + ε where ( β 1 + β 3 x 2 ) represents the change in E ( Y ) for every 1-unit increase in x 1 , holding x 2 fixed. ( β 2 + β 3 x 1 ) represents the change in E ( Y ) for every 1-unit increase in x 2 , holding x 1 fixed. No interaction between x 1 and x 2 Interaction between x 1 and x 2 Estimate the change...
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## This note was uploaded on 04/03/2014 for the course STAT 420 taught by Professor Stepanov during the Spring '08 term at University of Illinois, Urbana Champaign.

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