variety pesticide yield 1 1 1 49 2 1 1 39 3 1 2 50 4 1 2 55 5 1 3 43 6 1 3 38 7 1 4 43 8 1 4 58 9 2 1 55 10 2 1 41 11 2 2 67 12 2 2 58 13 2 3 53 14 2 3 42 15 2 4 85 16 2 4 73 17 3 1 66 18 3 1 68 19 3 2 85 20 3 2 92 21 3 3 69 22 3 3 62 23 3 4 85 24 3 4 89 Call: lm(formula = yield ~ variety + pesticide, data = orange) Residuals: Min 1Q Median 3Q Max -15.0000 -3.2604 0.9167 2.8750 14.6250 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 38.833 3.674 10.570 3.78e-09 *** variety2 12.375 3.674 3.368 0.003424 ** variety3 30.125 3.674 8.199 1.72e-07 *** pesticide2 14.833 4.242 3.496 0.002577 ** pesticide3 -1.833 4.242 -0.432 0.670773 pesticide4 19.167 4.242 4.518 0.000266 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.348 on 18 degrees of freedom Multiple R-squared: 0.8535, Adjusted R-squared: 0.8128 F-statistic: 20.97 on 5 and 18 DF, p-value: 6.213e-07 Analysis of Variance Table Response: yield Df Sum Sq Mean Sq F value Pr(>F) variety 2 3668.6 1834.29 33.971 7.755e-07 *** pesticide 3 1992.5 664.15 12.300 0.0001295 *** Residuals 18 971.9 54.00 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Call: lm(formula = yield ~ variety + pesticide + variety * pesticide, data = orange) Residuals: Min 1Q Median 3Q Max -7.50 -3.75 0.00 3.75 7.50 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 44.000 4.644 9.476 6.39e-07 *** variety2 4.000 6.567 0.609 0.55381 variety3 23.000 6.567 3.502 0.00436 ** pesticide2 8.500 6.567 1.294 0.21990 pesticide3 -3.500 6.567 -0.533 0.60378 pesticide4 6.500 6.567 0.990 0.34181 variety2:pesticide2 6.000 9.287 0.646 0.53040 variety3:pesticide2 13.000 9.287 1.400 0.18689 variety2:pesticide3 3.000 9.287 0.323 0.75223 variety3:pesticide3 2.000 9.287 0.215 0.83311 variety2:pesticide4 24.500 9.287 2.638 0.02165 * variety3:pesticide4 13.500 9.287 1.454 0.17170 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 6.567 on 12 degrees of freedom Multiple R-squared: 0.922, Adjusted R-squared: 0.8505 F-statistic: 12.89 on 11 and 12 DF, p-value: 5.22e-05 Analysis of Variance Table Response: yield Df Sum Sq Mean Sq F value Pr(>F) variety 2 3668.6 1834.29 42.5343 3.57e-06 *** pesticide 3 1992.5 664.15 15.4006 0.0002044 *** variety:pesticide 6 454.4 75.74 1.7562 0.1913174 Residuals 12 517.5 43.13 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Call: lm(formula = yield ~ variety, data = orange) Residuals: Min 1Q Median 3Q Max -18.250 -8.219 -2.562 8.031 25.750 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 46.875 4.201 11.159 2.75e-10 *** variety2 12.375 5.941 2.083 0.0496 * variety3 30.125 5.941 5.071 5.07e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.88 on 21 degrees of freedom Multiple R-squared: 0.5531, Adjusted R-squared: 0.5105 F-statistic: 12.99 on 2 and 21 DF, p-value: 0.0002125 Analysis of Variance Table Response: yield Df Sum Sq Mean Sq F value Pr(>F) variety 2 3668.6 1834.29 12.994 0.0002125 *** Residuals 21 2964.4 141.16 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Call: lm(formula = yield ~ pesticide, data = orange) Residuals: Min 1Q Median 3Q Max -29.17 -12.21 0.00 12.88 24.17 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 53.000 6.219 8.523 4.32e-08 *** pesticide2 14.833 8.794 1.687 0.1072 pesticide3 -1.833 8.794 -0.208 0.8370 pesticide4 19.167 8.794 2.179 0.0414 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 15.23 on 20 degrees of freedom Multiple R-squared: 0.3004, Adjusted R-squared: 0.1954 F-statistic: 2.862 on 3 and 20 DF, p-value: 0.06253 Analysis of Variance Table Response: yield Df Sum Sq Mean Sq F value Pr(>F) pesticide 3 1992.5 664.15 2.8624 0.06253 . Residuals 20 4640.5 232.02 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Call: lm(formula = yield ~ 1, data = orange) Residuals: Min 1Q Median 3Q Max -23.042 -13.542 -3.042 8.958 30.958 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 61.042 3.466 17.61 7.6e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 16.98 on 23 degrees of freedom Analysis of Variance Table Response: yield Df Sum Sq Mean Sq F value Pr(>F) Residuals 23 6633 288.39 Analysis of Variance Table Model 1: yield ~ variety + pesticide + variety * pesticide Model 2: yield ~ variety + pesticide Res.Df RSS Df Sum of Sq F Pr(>F) 1 12 517.50 2 18 971.92 -6 -454.42 1.7562 0.1913 Analysis of Variance Table Model 1: yield ~ variety + pesticide Model 2: yield ~ pesticide Res.Df RSS Df Sum of Sq F Pr(>F) 1 18 971.9 2 20 4640.5 -2 -3668.6 33.971 7.755e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Analysis of Variance Table Model 1: yield ~ variety + pesticide Model 2: yield ~ variety Res.Df RSS Df Sum of Sq F Pr(>F) 1 18 971.92 2 21 2964.38 -3 -1992.5 12.3 0.0001295 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1