Df Sum Sq Mean Sq F value Pr(>F)
Presion 3 0.016567 0.005522 82.83 2.3e-06 ***
Residuals 8 0.000533 0.000067
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
\[Y_{ij}=\mu + \tau_i+\epsilon_{ij}\]
Shapiro-Wilk normality test
data: res_anv
W = 0.94102, p-value = 0.5114
Bartlett test of homogeneity of variances
data: res_anv and data$Presion
Bartlett's K-squared = 0.95233, df = 3, p-value = 0.8128
diff lwr upr p adj
p_0.1-p_0.05 0.05000000 0.028650987 0.07134901 3.178083e-04
p_0.2-p_0.05 0.06666667 0.045317653 0.08801568 3.955617e-05
p_0.25-p_0.05 0.10333333 0.081984320 0.12468235 1.446558e-06
p_0.2-p_0.1 0.01666667 -0.004682347 0.03801568 1.344163e-01
p_0.25-p_0.1 0.05333333 0.031984320 0.07468235 2.012025e-04
p_0.25-p_0.2 0.03666667 0.015317653 0.05801568 2.553750e-03
Grubbs test for one outlier
data: data$t_fractura
G = 1.64859, U = 0.73046, p-value = 0.5021
alternative hypothesis: lowest value 0.87 is an outlier
Df Sum Sq Mean Sq F value Pr(>F)
Catalizador 3 192.5 64.15 14.5 3.01e-05 ***
Residuals 20 88.5 4.43
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
\[Y_{ij}=\mu + \tau_i+\epsilon_{ij}\]
Shapiro-Wilk normality test
data: res_anv2
W = 0.96494, p-value = 0.5454
Bartlett test of homogeneity of variances
data: res_anv2 and data2$Catalizador
Bartlett's K-squared = 2.9844, df = 3, p-value = 0.394
diff lwr upr p adj
B-A 5.0000000 1.600704 8.399296 0.0027710614
C-A 6.3333333 2.934037 9.732629 0.0002282162
D-A 0.1666667 -3.232629 3.565963 0.9990452190
C-B 1.3333333 -2.065963 4.732629 0.6948685671
D-B -4.8333333 -8.232629 -1.434037 0.0037860122
D-C -6.1666667 -9.565963 -2.767371 0.0003109873
Grubbs test for one outlier
data: data2$Rendimiento
G = 2.37238, U = 0.74466, p-value = 0.1413
alternative hypothesis: highest value 73 is an outlier
Df Sum Sq Mean Sq F value Pr(>F)
Bloque 1 42.67 42.67 4.848 0.0789 .
Sitio 2 229.56 114.78 13.043 0.0104 *
Residuals 5 44.00 8.80
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
\[Y_{ij}=\mu + \tau_i+\beta_j+\epsilon_{ij}\]
Shapiro-Wilk normality test
data: res_anv3
W = 0.85397, p-value = 0.08239
Bartlett test of homogeneity of variances
data: res_anv3 and data3$Sitio
Bartlett's K-squared = 0.060221, df = 2, p-value = 0.9703
diff lwr upr p adj
Norte-Centro 5.333333 -2.548031 13.2146978 0.163748483
Sur-Centro -7.000000 -14.881364 0.8813645 0.074426891
Sur-Norte -12.333333 -20.214698 -4.4519689 0.008743626
Grubbs test for one outlier
data: data3$Duracion
G = 1.83798, U = 0.52495, p-value = 0.18
alternative hypothesis: highest value 96 is an outlier
Analysis of Variance Table
Response: Ventas
Df Sum Sq Mean Sq F value Pr(>F)
Region 2 2163 1081.4 2.9920 0.25050
Producto 2 32380 16189.8 44.7919 0.02184 *
Estacion 2 1506 752.8 2.0827 0.32439
Residuals 2 723 361.4
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
\[Y_{ijk}=\mu + \tau_i+\beta_j+\delta_k+\epsilon_{ijk}\]
Shapiro-Wilk normality test
data: res_anv4
W = 0.70608, p-value = 0.001671
Bartlett test of homogeneity of variances
data: res_anv4 and data4$Producto
Bartlett's K-squared = 0, df = 2, p-value = 1
Grubbs test for one outlier
data: data4$Ventas
G = 1.60611, U = 0.63724, p-value = 0.3875
alternative hypothesis: highest value 410 is an outlier