> summary(muscle2.reg) Call: lm(formula = H ~ M + W, data = muscle2) Residuals: Min 1Q Median 3Q Max -282.0 -109.2 9.1 123.9 235.9 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 977.425 376.053 2.599 0.013723 * M 17.778 4.943 3.597 0.001011 ** W 6.244 1.522 4.102 0.000242 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 147.1 on 34 degrees of freedom Multiple R-squared: 0.4922, Adjusted R-squared: 0.4624 F-statistic: 16.48 on 2 and 34 DF, p-value: 9.914e-06 > > aov(muscle2.reg) Call: aov(formula = muscle2.reg) Terms: M W Residuals Sum of Squares 349284.5 364235.6 736023.6 Deg. of Freedom 1 1 34 Residual standard error: 147.1318 Estimated effects may be unbalanced > > par(mfrow=c(2,2)) > > plot(muscle2.reg, which=1:4) > > muscle2.rstandard <- rstandard(muscle2.reg) > > muscle2.rstudent <- rstudent(muscle2.reg) > > muscle2.inf <- influence.measures(muscle2.reg) > > muscle2.rstandard 1 2 3 4 5 6 0.64088516 0.21676409 -0.39596519 -0.87498523 -1.99874900 0.90822206 7 8 9 10 11 12 1.14225776 -0.22325885 0.96018174 -0.23991633 1.50815450 0.14412538 13 14 15 16 17 18 -0.76707120 -0.90691533 1.60806293 0.87760899 1.63050403 -1.25108759 19 20 21 22 23 24 0.43127694 0.22295904 -0.32516622 0.68916608 1.55698960 1.26066295 25 26 27 28 29 30 1.03930157 -0.41124039 0.28542219 -0.65953210 -1.05700926 -0.11278928 31 32 33 34 35 36 -1.69682257 -0.77708127 -1.24130914 -1.79025245 0.19102184 0.07203953 37 -0.86044390 > > muscle2.rstudent 1 2 3 4 5 6 0.63523866 0.21370031 -0.39100130 -0.87189416 -2.09613088 0.90582162 7 8 9 10 11 12 1.14756891 -0.22011253 0.95904825 -0.23656214 1.53815029 0.14203346 13 14 15 16 17 18 -0.76233173 -0.90448601 1.64815278 0.87456915 1.67308982 -1.26194146 19 20 21 22 23 24 0.42605428 0.21981651 -0.32084795 0.68374812 1.59171611 1.27207161 25 26 27 28 29 30 1.04056596 -0.40615900 0.28153096 -0.65395742 -1.05889237 -0.11113903 31 32 33 34 35 36 -1.74730240 -0.77245866 -1.25160803 -1.85323788 0.18829280 0.07097764 37 -0.85707886 > > muscle2.inf Influence measures of lm(formula = H ~ M + W, data = muscle2) : dfb.1_ dfb.M dfb.W dffit cov.r cook.d hat inf 1 -0.227811 0.16922 0.155829 0.2673 1.241 0.024249 0.1505 2 0.001412 0.02762 -0.044410 0.0621 1.181 0.001323 0.0779 3 0.030986 -0.01523 -0.040243 -0.0793 1.123 0.002151 0.0395 4 -0.239043 0.33475 -0.132321 -0.3829 1.218 0.049207 0.1616 5 0.479499 -0.45062 -0.184612 -0.6205 0.818 0.116686 0.0806 6 0.007809 0.02676 -0.041171 0.1583 1.047 0.008401 0.0296 7 -0.074315 0.02501 0.119971 0.2297 1.012 0.017429 0.0385 8 -0.016397 0.05422 -0.064739 -0.0896 1.269 0.002755 0.1422 * 9 0.013783 0.02179 -0.042943 0.1666 1.038 0.009277 0.0293 10 -0.010028 0.02497 -0.027809 -0.0535 1.144 0.000982 0.0487 11 -0.053259 0.12449 -0.077822 0.2935 0.921 0.027598 0.0351 12 -0.009198 0.00309 0.014849 0.0284 1.135 0.000277 0.0385 13 -0.070523 0.12609 -0.094729 -0.1988 1.108 0.013341 0.0637 14 -0.254132 0.17604 0.161166 -0.2960 1.125 0.029354 0.0967 15 -0.321034 0.28863 0.150237 0.4406 0.924 0.061597 0.0667 16 0.199413 -0.20723 -0.007693 0.2573 1.109 0.022219 0.0797 17 -0.116027 0.10230 0.079072 0.3110 0.886 0.030628 0.0334 18 0.378448 -0.56839 0.216104 -0.6348 1.190 0.132041 0.2020 19 0.097146 -0.10096 -0.003747 0.1253 1.169 0.005366 0.0797 20 0.018432 -0.02651 0.014356 0.0470 1.139 0.000757 0.0437 21 -0.083948 0.08937 0.000582 -0.1056 1.201 0.003820 0.0978 22 0.041063 0.13548 -0.288112 0.3320 1.296 0.037335 0.1908 * 23 0.005476 0.04521 -0.053933 0.2739 0.902 0.023928 0.0288 24 -0.039593 0.08835 -0.048285 0.2337 0.979 0.017876 0.0326 25 -0.222333 0.06569 0.326994 0.3871 1.130 0.049834 0.1216 26 -0.029455 0.02171 0.010525 -0.0723 1.112 0.001788 0.0307 27 0.008061 -0.01549 0.016496 0.0518 1.123 0.000919 0.0327 28 0.041216 -0.07022 0.027315 -0.1318 1.095 0.005893 0.0391 29 0.186894 -0.14127 -0.134178 -0.2727 1.055 0.024706 0.0622 30 0.000896 -0.00467 0.003916 -0.0194 1.126 0.000130 0.0297 31 0.052339 0.06941 -0.249024 -0.3881 0.880 0.047357 0.0470 32 0.136778 -0.01551 -0.245551 -0.2831 1.176 0.027042 0.1184 33 -0.150290 0.06976 0.138596 -0.2654 0.994 0.023101 0.0430 34 0.228751 -0.43587 0.263445 -0.5842 0.893 0.106157 0.0904 35 0.038447 0.00418 -0.074678 0.0820 1.297 0.002308 0.1595 * 36 0.039372 -0.02665 -0.027292 0.0424 1.483 0.000617 0.2629 * 37 -0.032102 -0.08445 0.183504 -0.2441 1.107 0.020025 0.0751 > > > ## You must have downloaded DAAG to get Variance Inflation Factors > > library(DAAG) > > muscle2.vif <- vif(muscle2.reg) > > muscle2.vif M W 1.0096 1.0096 > > dev.off() null device 1 >