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9719 adj r squared 09718 f stat 6229 on 1 and 180 df

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Unformatted text preview: repwt, subset = -12) Residuals: Min 1Q Median -7.5296 -1.1010 -0.1322 3Q 1.1287 Max 6.3891 Coefficients: Estimate Std. Err. t value Pr(>|t|) (Intercept) 2.73380 0.81479 3.355 0.000967 *** repwt 0.95837 0.01214 78.926 < 2e-16 *** --Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 Residual standard error: 2.254 on 180 degrees of freedom (17 observations deleted due to missingness) Multiple R-squared: 0.9719, Adj. R-squared: 0.9718 F-stat.: 6229 on 1 and 180 DF, p-value: < 2.2e-16 20 Multiple Regression We use the First babies data set of the package UsingR. install.packages("UsingR") For each new R session execute Examine that data frame library(UsingR) babies. Here we focus on wt # baby’s weight at birth (ounces) gestation # pregnancy length (days), 999 = unknown age # mother at end of pregnancy (yrs), 99 = unkown ht # mother’s height (inches), 99 = unknown wt1 # mother’s prepreg. weight (lbs), 999 = unknown dage # father’s age (yrs), 99 = unknown dht # father’s height (inches), 99 = unknown dwt # father’s weight (lbs), 999 = unknown 21 Simple Linear Regression > attach(babies)# -> wt, gestation visible to plot > wtgest.reg <- lm(wt ~ gestation, data = babies, + subset = gestation < 999) > plot(gestation[gestation < 999], + wt[gestation<999]) > abline(wtgest.reg,col="blue") > summary(wtgest.reg) 22 180 Weight vs Gestation qq q q q q q q q q q q q qq qq q q qq q q q q q qqqqq qq q q q q qq q qq q q q q q q qqq qq q qq q q qqq q q q qq qq qq qq q q q qqq q q qq qq q q q qq q qq q q q qqqqqqqqqq q q q q q q qq q q q qq q q q q qq q q q qqq qq qqq q qq qqqqq qqq qq q qq q qq q qq q qq qqqqqqqqqqq qq q qqqqqqqq qqq q q q q q qq q q q q q qqqq qqqqq q q q q q qq qqqq qq q q q qqqq q q q qq qq q qq q q qq q q q q q q q qqq q qqqqqqqqqqqq q q q q qqqqq qqqqq q q q qqqqqq qq q qq q q qq qq q q q q qqqqqqqqqqq qq q q q qqqqq qqq q q qq q qq qqqq qqqq...
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