Call: lm(formula = output[type == 1] ~ yarncount[type == 1]) Residuals: Min 1Q Median 3Q Max -22.106 -12.975 -4.958 3.881 76.156 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 110.75705 6.05564 18.29 < 2e-16 *** yarncount[type == 1] -0.90941 0.08911 -10.21 8.05e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 19.73 on 41 degrees of freedom Multiple R-squared: 0.7175, Adjusted R-squared: 0.7107 F-statistic: 104.2 on 1 and 41 DF, p-value: 8.047e-13 > anova(spindle.reg) Analysis of Variance Table Response: output[type == 1] Df Sum Sq Mean Sq F value Pr(>F) yarncount[type == 1] 1 40543 40543 104.15 8.047e-13 *** Residuals 41 15960 389 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > spindle.aov <- lm(output[type==1] ~ factor(yarncount[type==1])) > summary(spindle.aov) Call: lm(formula = output[type == 1] ~ factor(yarncount[type == 1])) Residuals: Min 1Q Median 3Q Max -7.7 -0.5 0.0 1.0 7.7 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 176.000 5.197 33.868 2.79e-13 *** factor(yarncount[type == 1])17 -40.000 7.349 -5.443 0.000149 *** factor(yarncount[type == 1])20 -54.100 7.349 -7.361 8.72e-06 *** factor(yarncount[type == 1])23 -70.550 6.365 -11.085 1.16e-07 *** factor(yarncount[type == 1])25 -73.950 6.365 -11.619 6.92e-08 *** factor(yarncount[type == 1])28 -91.700 7.349 -12.478 3.13e-08 *** factor(yarncount[type == 1])30 -97.000 7.349 -13.199 1.66e-08 *** factor(yarncount[type == 1])31 -96.000 7.349 -13.063 1.87e-08 *** factor(yarncount[type == 1])33 -96.000 7.349 -13.063 1.87e-08 *** factor(yarncount[type == 1])34 -101.000 7.349 -13.743 1.05e-08 *** factor(yarncount[type == 1])35 -106.900 7.349 -14.546 5.52e-09 *** factor(yarncount[type == 1])38 -113.300 6.365 -17.802 5.41e-10 *** factor(yarncount[type == 1])39 -111.500 7.349 -15.172 3.41e-09 *** factor(yarncount[type == 1])40 -111.400 6.001 -18.565 3.32e-10 *** factor(yarncount[type == 1])45 -122.550 6.365 -19.255 2.17e-10 *** factor(yarncount[type == 1])48 -131.000 7.349 -17.825 5.32e-10 *** factor(yarncount[type == 1])50 -126.000 7.349 -17.145 8.35e-10 *** factor(yarncount[type == 1])55 -124.750 6.365 -19.601 1.76e-10 *** factor(yarncount[type == 1])60 -135.400 7.349 -18.424 3.63e-10 *** factor(yarncount[type == 1])65 -139.700 7.349 -19.009 2.52e-10 *** factor(yarncount[type == 1])70 -143.000 7.349 -19.458 1.92e-10 *** factor(yarncount[type == 1])75 -147.150 6.365 -23.120 2.55e-11 *** factor(yarncount[type == 1])80 -147.500 6.365 -23.175 2.48e-11 *** factor(yarncount[type == 1])85 -148.500 6.365 -23.333 2.29e-11 *** factor(yarncount[type == 1])87 -150.000 7.349 -20.411 1.10e-10 *** factor(yarncount[type == 1])90 -149.850 6.365 -23.545 2.06e-11 *** factor(yarncount[type == 1])100 -154.350 6.365 -24.252 1.45e-11 *** factor(yarncount[type == 1])110 -161.000 7.349 -21.907 4.80e-11 *** factor(yarncount[type == 1])125 -166.000 7.349 -22.588 3.35e-11 *** factor(yarncount[type == 1])130 -160.300 7.349 -21.812 5.06e-11 *** factor(yarncount[type == 1])160 -166.000 7.349 -22.588 3.35e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.197 on 12 degrees of freedom Multiple R-squared: 0.9943, Adjusted R-squared: 0.9799 F-statistic: 69.34 on 30 and 12 DF, p-value: 1.288e-09 > anova(spindle.aov) Analysis of Variance Table Response: output[type == 1] Df Sum Sq Mean Sq F value Pr(>F) factor(yarncount[type == 1]) 30 56178 1872.6 69.344 1.288e-09 *** Residuals 12 324 27.0 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(spindle.reg,spindle.aov) Analysis of Variance Table Model 1: output[type == 1] ~ yarncount[type == 1] Model 2: output[type == 1] ~ factor(yarncount[type == 1]) Res.Df RSS Df Sum of Sq F Pr(>F) 1 41 15959.5 2 12 324.1 29 15636 19.965 1.799e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1