> rabbits trt tempdiff 1 1 2.2 2 1 1.6 3 1 0.8 4 1 1.8 5 1 1.4 6 1 0.4 7 1 0.6 8 1 1.5 9 1 0.5 10 2 0.3 11 2 0.0 12 2 0.6 13 2 0.0 14 2 -0.3 15 2 0.2 16 3 0.1 17 3 0.1 18 3 0.2 19 3 -0.4 20 3 0.3 21 3 0.1 22 3 0.1 23 3 -0.5 > > tapply(tempdiff,trt,mean) 1 2 3 1.2000000 0.1333333 0.0000000 > tapply(tempdiff,trt,sd) 1 2 3 0.6422616 0.3076795 0.2878492 > > rabbits.mod1 <- lm(tempdiff~trt) > > summary(rabbits.mod1) Call: lm(formula = tempdiff ~ trt) Residuals: Min 1Q Median 3Q Max -0.80 -0.40 0.10 0.25 1.00 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.2000 0.1555 7.716 2.03e-07 *** trt2 -1.0667 0.2459 -4.338 0.000319 *** trt3 -1.2000 0.2267 -5.293 3.52e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4665 on 20 degrees of freedom Multiple R-squared: 0.6237, Adjusted R-squared: 0.5861 F-statistic: 16.58 on 2 and 20 DF, p-value: 5.689e-05 > anova(rabbits.mod1) Analysis of Variance Table Response: tempdiff Df Sum Sq Mean Sq F value Pr(>F) trt 2 7.2162 3.6081 16.576 5.689e-05 *** Residuals 20 4.3533 0.2177 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > predict(rabbits.mod1) 1 2 3 4 5 6 7 8 1.2000000 1.2000000 1.2000000 1.2000000 1.2000000 1.2000000 1.2000000 1.2000000 9 10 11 12 13 14 15 16 1.2000000 0.1333333 0.1333333 0.1333333 0.1333333 0.1333333 0.1333333 0.0000000 17 18 19 20 21 22 23 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 >