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# Note that here the cases used are specied by a logic

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Unformatted text preview: 221 deg. of freedom Multiple R-squared: 0.1663, Adj. R-squared: 0.1657 F-stat.: 243.6 on 1 & 1221 DF, p-value: < 2.2e-16 24 Multiple Regression > wtreg <- lm(wt ~ gestation + age + ht + + wt1 + dage + dht + dwt, data = babies, + subset = gestation < 999 & age < 99 & ht < 99 & + wt1 < 999 & dage < 99 & dht < 99 & dwt < 999) Here we t the following model, again by least squares yi = β0 + β1 xi 1 + . . . + βp xip + where i with i ∼ i .i .d . N (0, σ 2 ) yi ∼ wti , xi 1 ∼ gestationi , . . ., xip ∼ dwti , and p = 7. Note that here the cases used are specied by a logic vector, only cases with Compare with TRUE enter the analysis. subset = -12 (omitting as in the previous usage. 25 case 12), Multiple Regression (Summary) > summary(wtreg) Call: lm(formula = wt ~ gestation + age + ht + wt1 + dage + dht + dwt, data = babies, subset = gestation < 999 & age < 99 & ht < 99 & wt1 < 999 & dage < 99 & dht < 99 & dwt < 999) Residuals: Min 1Q -48.675 -10.528 26 Median 0.403 3Q 10.123 Max 54.960 Multiple Regression (Summary Cont.) Coefficients: Estimate Std. Error (Interc) -101.9075 23.2918 gestation 0.4503 0.0391 age 0.1350 0.1881 ht 1.2230 0.2852 wt1 0.0308 0.0343 dage 0.0603 0.1655 dht -0.0783 0.2706 dwt 0.0783 0.0331 --Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ t value Pr(>|t|) -4.375 1.40e-05 *** 11.520 < 2e-16 *** 0.717 0.4733 4.289 2.05e-05 *** 0.898 0.3693 0...
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## This note was uploaded on 02/25/2014 for the course STAT 302 taught by Professor Fritz during the Winter '13 term at University of Washington.

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