> Dataset <- within(Dataset, { + RS_TESTAB <- Recode(S_TESTAB, '1:2=1; 3=2; 4:5=3;', as.factor.result=FALSE) + RS_TBANCO <- Recode(S_TBANCO, '1:2=1; 3=2; 4:5=3;', as.factor.result=FALSE) + RS_MOVIL <- Recode(S_MOVIL, '1:2=1; 3=2; 4:5=3;', as.factor.result=FALSE) + RS_EFECTIVO <- Recode(S_EFECTIVO, '1:2=1; 3=2; 4:5=3;', as.factor.result=FALSE) + RS_CLESS <- Recode(S_CLESS, '1:2=1; 3=2; 4:5=3;', as.factor.result=FALSE) + }) > AnovaModel.1 <- aov(RS_EFECTIVO ~ HABITAT, data=Dataset) > summary(AnovaModel.1) Df Sum Sq Mean Sq F value Pr(>F) HABITAT 1 0.83 0.8302 1.115 0.293 Residuals 124 92.28 0.7442 > with(Dataset, numSummary(RS_EFECTIVO, groups=HABITAT, statistics=c("mean", "sd"))) mean sd data:n H1 2.360656 0.8950379 61 H2 2.523077 0.8311831 65 > AnovaModel.2 <- aov(RS_EFECTIVO ~ EDAD, data=Dataset) > summary(AnovaModel.2) Df Sum Sq Mean Sq F value Pr(>F) EDAD 3 2.17 0.7248 0.972 0.408 Residuals 122 90.94 0.7454 > with(Dataset, numSummary(RS_EFECTIVO, groups=EDAD, statistics=c("mean", "sd"))) mean sd data:n E1 2.687500 0.7041543 16 E2 2.365079 0.9034269 63 E3 2.566667 0.8172002 30 E4 2.294118 0.9195587 17 > AnovaModel.3 <- aov(RS_EFECTIVO ~ ESTUDIOS, data=Dataset) > summary(AnovaModel.3) Df Sum Sq Mean Sq F value Pr(>F) ESTUDIOS 2 2.24 1.1177 1.513 0.224 Residuals 123 90.88 0.7388 > with(Dataset, numSummary(RS_EFECTIVO, groups=ESTUDIOS, statistics=c("mean", "sd"))) mean sd data:n F1 2.421053 0.9015905 19 F2 2.282609 0.9107517 46 F3 2.573770 0.8053781 61 > AnovaModel.4 <- aov(RS_EFECTIVO ~ SITUACION, data=Dataset) > summary(AnovaModel.4) Df Sum Sq Mean Sq F value Pr(>F) SITUACION 4 2.49 0.6227 0.831 0.508 Residuals 121 90.62 0.7489 > with(Dataset, numSummary(RS_EFECTIVO, groups=SITUACION, statistics=c("mean", "sd"))) mean sd data:n S1 2.600000 0.8432740 10 S2 2.000000 1.0690450 8 S3 2.590909 0.7341397 22 S4 2.410959 0.8793245 73 S5 2.538462 0.8770580 13 > AnovaModel.5 <- aov(RS_EFECTIVO ~ SEXO, data=Dataset) > summary(AnovaModel.5) Df Sum Sq Mean Sq F value Pr(>F) SEXO 1 13.63 13.634 21.27 0.00000978 *** Residuals 124 79.48 0.641 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > with(Dataset, numSummary(RS_EFECTIVO, groups=SEXO, statistics=c("mean", "sd"))) mean sd data:n H 2.818182 0.5474151 55 M 2.154930 0.9509076 71 > AnovaModel.6 <- aov(RS_TBANCO ~ HABITAT, data=Dataset) > summary(AnovaModel.6) Df Sum Sq Mean Sq F value Pr(>F) HABITAT 1 1.03 1.0319 2.387 0.125 Residuals 124 53.60 0.4323 > with(Dataset, numSummary(RS_TBANCO, groups=HABITAT, statistics=c("mean", "sd"))) mean sd data:n H1 2.557377 0.7421723 61 H2 2.738462 0.5667044 65 > AnovaModel.7 <- aov(RS_TBANCO ~ EDAD, data=Dataset) > summary(AnovaModel.7) Df Sum Sq Mean Sq F value Pr(>F) EDAD 3 2.08 0.6933 1.609 0.191 Residuals 122 52.56 0.4308 > with(Dataset, numSummary(RS_TBANCO, groups=EDAD, statistics=c("mean", "sd"))) mean sd data:n E1 2.937500 0.2500000 16 E2 2.666667 0.6221710 63 E3 2.500000 0.8200084 30 E4 2.588235 0.7122871 17 > AnovaModel.8 <- aov(RS_TBANCO ~ ESTUDIOS, data=Dataset) > summary(AnovaModel.8) Df Sum Sq Mean Sq F value Pr(>F) ESTUDIOS 2 0.05 0.0269 0.061 0.941 Residuals 123 54.58 0.4437 > with(Dataset, numSummary(RS_TBANCO, groups=ESTUDIOS, statistics=c("mean", "sd"))) mean sd data:n F1 2.631579 0.6839856 19 F2 2.630435 0.6785179 46 F3 2.672131 0.6511864 61 > AnovaModel.9 <- aov(RS_TBANCO ~ SITUACION, data=Dataset) > summary(AnovaModel.9) Df Sum Sq Mean Sq F value Pr(>F) SITUACION 4 1.57 0.3913 0.892 0.471 Residuals 121 53.07 0.4386 > with(Dataset, numSummary(RS_TBANCO, groups=SITUACION, statistics=c("mean", "sd"))) mean sd data:n S1 2.800000 0.4216370 10 S2 2.250000 0.8864053 8 S3 2.636364 0.7267314 22 S4 2.671233 0.6678072 73 S5 2.692308 0.4803845 13 > AnovaModel.10 <- aov(RS_TBANCO ~ SEXO, data=Dataset) > summary(AnovaModel.10) Df Sum Sq Mean Sq F value Pr(>F) SEXO 1 0.33 0.3317 0.757 0.386 Residuals 124 54.30 0.4379 > with(Dataset, numSummary(RS_TBANCO, groups=SEXO, statistics=c("mean", "sd"))) mean sd data:n H 2.709091 0.5985392 55 M 2.605634 0.7066798 71 > AnovaModel.11 <- aov(RS_TESTAB ~ HABITAT, data=Dataset) > summary(AnovaModel.11) Df Sum Sq Mean Sq F value Pr(>F) HABITAT 1 2.82 2.8203 6.247 0.0137 * Residuals 124 55.98 0.4515 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > with(Dataset, numSummary(RS_TESTAB, groups=HABITAT, statistics=c("mean", "sd"))) mean sd data:n H1 2.114754 0.6855256 61 H2 1.815385 0.6588889 65 > AnovaModel.12 <- aov(RS_TESTAB ~ ESTUDIOS, data=Dataset) > summary(AnovaModel.12) Df Sum Sq Mean Sq F value Pr(>F) ESTUDIOS 2 1.71 0.8564 1.845 0.162 Residuals 123 57.09 0.4641 > with(Dataset, numSummary(RS_TESTAB, groups=ESTUDIOS, statistics=c("mean", "sd"))) mean sd data:n F1 1.684211 0.6710383 19 F2 2.000000 0.6992059 46 F3 2.016393 0.6706167 61 > AnovaModel.13 <- aov(RS_TESTAB ~ EDAD, data=Dataset) > summary(AnovaModel.13) Df Sum Sq Mean Sq F value Pr(>F) EDAD 3 2.88 0.9600 2.094 0.104 Residuals 122 55.92 0.4584 > with(Dataset, numSummary(RS_TESTAB, groups=EDAD, statistics=c("mean", "sd"))) mean sd data:n E1 1.875000 0.7187953 16 E2 1.841270 0.7003766 63 E3 2.100000 0.6617636 30 E4 2.235294 0.5622957 17 > AnovaModel.14 <- aov(RS_TESTAB ~ SITUACION, data=Dataset) > summary(AnovaModel.14) Df Sum Sq Mean Sq F value Pr(>F) SITUACION 4 1.04 0.2588 0.542 0.705 Residuals 121 57.77 0.4774 > with(Dataset, numSummary(RS_TESTAB, groups=SITUACION, statistics=c("mean", "sd"))) mean sd data:n S1 1.800000 0.4216370 10 S2 2.125000 0.6408699 8 S3 2.090909 0.7501804 22 S4 1.945205 0.6643796 73 S5 1.846154 0.8987170 13 > AnovaModel.14 <- aov(RS_TESTAB ~ SEXO, data=Dataset) > summary(AnovaModel.14) Df Sum Sq Mean Sq F value Pr(>F) SEXO 1 1.09 1.0920 2.346 0.128 Residuals 124 57.71 0.4654 > with(Dataset, numSummary(RS_TESTAB, groups=SEXO, statistics=c("mean", "sd"))) mean sd data:n H 1.854545 0.6503043 55 M 2.042254 0.7058251 71 > AnovaModel.15 <- aov(RS_CLESS ~ HABITAT, data=Dataset) > summary(AnovaModel.15) Df Sum Sq Mean Sq F value Pr(>F) HABITAT 1 0.02 0.0184 0.036 0.85 Residuals 124 63.70 0.5137 > with(Dataset, numSummary(RS_CLESS, groups=HABITAT, statistics=c("mean", "sd"))) mean sd data:n H1 1.393443 0.7136453 61 H2 1.369231 0.7195752 65 > AnovaModel.16 <- aov(RS_CLESS ~ ESTUDIOS, data=Dataset) > summary(AnovaModel.16) Df Sum Sq Mean Sq F value Pr(>F) ESTUDIOS 2 1.24 0.6210 1.223 0.298 Residuals 123 62.47 0.5079 > with(Dataset, numSummary(RS_CLESS, groups=ESTUDIOS, statistics=c("mean", "sd"))) mean sd data:n F1 1.578947 0.8377078 19 F2 1.413043 0.7476210 46 F3 1.295082 0.6414635 61 > AnovaModel.17 <- aov(RS_CLESS ~ EDAD, data=Dataset) > summary(AnovaModel.17) Df Sum Sq Mean Sq F value Pr(>F) EDAD 3 2.34 0.7784 1.547 0.206 Residuals 122 61.38 0.5031 > with(Dataset, numSummary(RS_CLESS, groups=EDAD, statistics=c("mean", "sd"))) mean sd data:n E1 1.062500 0.2500000 16 E2 1.380952 0.7279793 63 E3 1.533333 0.7760792 30 E4 1.411765 0.7952062 17 > AnovaModel.18 <- aov(RS_CLESS ~ SITUACION, data=Dataset) > summary(AnovaModel.18) Df Sum Sq Mean Sq F value Pr(>F) SITUACION 4 2.33 0.5829 1.149 0.337 Residuals 121 61.38 0.5073 > with(Dataset, numSummary(RS_CLESS, groups=SITUACION, statistics=c("mean", "sd"))) mean sd data:n S1 1.200000 0.6324555 10 S2 1.875000 0.9910312 8 S3 1.363636 0.7267314 22 S4 1.356164 0.6743281 73 S5 1.384615 0.7679476 13 > AnovaModel.19 <- aov(RS_CLESS ~ SEXO, data=Dataset) > summary(AnovaModel.19) Df Sum Sq Mean Sq F value Pr(>F) SEXO 1 2.59 2.586 5.246 0.0237 * Residuals 124 61.13 0.493 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > with(Dataset, numSummary(RS_CLESS, groups=SEXO, statistics=c("mean", "sd"))) mean sd data:n H 1.218182 0.5673496 55 M 1.507042 0.7905376 71 > AnovaModel.20 <- aov(RS_MOVIL ~ HABITAT, data=Dataset) > summary(AnovaModel.20) Df Sum Sq Mean Sq F value Pr(>F) HABITAT 1 0.01 0.0125 0.019 0.892 Residuals 124 82.98 0.6692 > with(Dataset, numSummary(RS_MOVIL, groups=HABITAT, statistics=c("mean", "sd"))) mean sd data:n H1 1.573770 0.8457544 61 H2 1.553846 0.7911773 65 > AnovaModel.21 <- aov(RS_MOVIL ~ ESTUDIOS, data=Dataset) > summary(AnovaModel.21) Df Sum Sq Mean Sq F value Pr(>F) ESTUDIOS 2 1.73 0.8645 1.308 0.274 Residuals 123 81.26 0.6607 > with(Dataset, numSummary(RS_MOVIL, groups=ESTUDIOS, statistics=c("mean", "sd"))) mean sd data:n F1 1.684211 0.8852264 19 F2 1.673913 0.8957765 46 F3 1.442623 0.7193652 61 > AnovaModel.22 <- aov(RS_MOVIL ~ EDAD, data=Dataset) > summary(AnovaModel.22) Df Sum Sq Mean Sq F value Pr(>F) EDAD 3 4.58 1.5276 2.377 0.0732 . Residuals 122 78.41 0.6427 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > with(Dataset, numSummary(RS_MOVIL, groups=EDAD, statistics=c("mean", "sd"))) mean sd data:n E1 1.437500 0.6291529 16 E2 1.746032 0.8793006 63 E3 1.300000 0.6512587 30 E4 1.470588 0.8744746 17 > AnovaModel.23 <- aov(RS_MOVIL ~ SITUACION, data=Dataset) > summary(AnovaModel.23) Df Sum Sq Mean Sq F value Pr(>F) SITUACION 4 1.83 0.4571 0.681 0.606 Residuals 121 81.16 0.6708 > with(Dataset, numSummary(RS_MOVIL, groups=SITUACION, statistics=c("mean", "sd"))) mean sd data:n S1 1.600000 0.9660918 10 S2 1.750000 1.0350983 8 S3 1.318182 0.5679004 22 S4 1.616438 0.8600733 73 S5 1.538462 0.6602253 13 > AnovaModel.24 <- aov(RS_MOVIL ~ SEXO, data=Dataset) > summary(AnovaModel.24) Df Sum Sq Mean Sq F value Pr(>F) SEXO 1 2.61 2.6090 4.025 0.047 * Residuals 124 80.38 0.6483 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > with(Dataset, numSummary(RS_MOVIL, groups=SEXO, statistics=c("mean", "sd"))) mean sd data:n H 1.400000 0.6831301 55 M 1.690141 0.8878795 71