library(readxl)
library(readxl)
datos_tesis1 <- read_excel("C:/Users/Isabella/Desktop/datos tesis1.xlsx",
col_types = c("numeric", "text", "text",
"text", "numeric", "text", "text",
"numeric", "text", "text", "numeric",
"text", "text", "numeric", "text",
"text", "text", "text", "text", "text",
"numeric", "text", "text", "text",
"text", "text", "text"))
modelo1=glm(formula= Y~EDAD, family="binomial", data = datos_tesis1)
summary(modelo1)
##
## Call:
## glm(formula = Y ~ EDAD, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2837 -1.2624 0.7913 1.0403 1.1535
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.04836 0.12917 -0.374 0.708
## EDAD 0.06233 0.01233 5.053 4.34e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 917.58 on 714 degrees of freedom
## AIC: 921.58
##
## Number of Fisher Scoring iterations: 4
modelo2=glm(formula= Y~EMPLEADOS, family="binomial", data = datos_tesis1)
summary(modelo2)
##
## Call:
## glm(formula = Y ~ EMPLEADOS, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3518 -1.3665 0.9267 0.9994 0.9994
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.43427 0.08143 5.333 9.65e-08 ***
## EMPLEADOS 0.18884 0.07666 2.463 0.0138 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 939.84 on 714 degrees of freedom
## AIC: 943.84
##
## Number of Fisher Scoring iterations: 4
modelo3=glm(formula =Y ~+DEUDAASUMIDA, family = "binomial", data=datos_tesis1)
summary(modelo3)
##
## Call:
## glm(formula = Y ~ +DEUDAASUMIDA, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3119 -1.0654 0.3784 1.2936 1.2936
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2692 0.0961 -2.801 0.00509 **
## DEUDAASUMIDA1 2.8699 0.2565 11.190 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 741.64 on 714 degrees of freedom
## AIC: 745.64
##
## Number of Fisher Scoring iterations: 5
modelo4a=glm(formula =Y ~X1, family = "binomial", data=datos_tesis1)
summary(modelo4a)
##
## Call:
## glm(formula = Y ~ X1, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7825 -1.0858 0.2052 1.2719 1.2719
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.21936 0.09222 -2.379 0.0174 *
## X11 4.06951 0.46124 8.823 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 702.79 on 714 degrees of freedom
## AIC: 706.79
##
## Number of Fisher Scoring iterations: 6
modelo4b=glm(formula =Y ~X2, family = "binomial", data=datos_tesis1)
summary(modelo4b)
##
## Call:
## glm(formula = Y ~ X2, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4003 -1.4003 0.9698 0.9698 1.0579
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.51007 0.07757 6.576 4.84e-11 ***
## X21 -0.22239 0.76769 -0.290 0.772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 947.78 on 714 degrees of freedom
## AIC: 951.78
##
## Number of Fisher Scoring iterations: 4
modelo4c=glm(formula =Y ~X3, family = "binomial", data=datos_tesis1)
summary(modelo4c)
##
## Call:
## glm(formula = Y ~ X3, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4023 -1.4023 0.9681 0.9681 1.1127
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.51462 0.07794 6.603 4.04e-11 ***
## X31 -0.36047 0.56178 -0.642 0.521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 947.46 on 714 degrees of freedom
## AIC: 951.46
##
## Number of Fisher Scoring iterations: 4
modelo4d=glm(formula =Y ~X4, family = "binomial", data=datos_tesis1)
summary(modelo4d)
##
## Call:
## glm(formula = Y ~ X4, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4006 -1.4006 0.9695 0.9695 1.0383
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.51083 0.07785 6.562 5.32e-11 ***
## X41 -0.17435 0.59069 -0.295 0.768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 947.78 on 714 degrees of freedom
## AIC: 951.78
##
## Number of Fisher Scoring iterations: 4
modelo4e=glm(formula =Y ~X5, family = "binomial", data=datos_tesis1)
summary(modelo4e)
##
## Call:
## glm(formula = Y ~ X5, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3963 -1.3963 0.9733 0.9733 0.9733
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.50111 0.07726 6.486 8.83e-11 ***
## X51 14.06495 509.65213 0.028 0.978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 945.03 on 714 degrees of freedom
## AIC: 949.03
##
## Number of Fisher Scoring iterations: 13
modelo5=glm(formula =Y ~CC, family = "binomial", data=datos_tesis1)
summary(modelo5)
##
## Call:
## glm(formula = Y ~ CC, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5928 -1.3348 0.8127 1.0278 1.0278
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.36270 0.08881 4.084 4.43e-05 ***
## CC1 0.57557 0.18345 3.138 0.0017 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 937.61 on 714 degrees of freedom
## AIC: 941.61
##
## Number of Fisher Scoring iterations: 4
modelo6=glm(formula =Y ~FORMAJURIDICA, family = "binomial", data=datos_tesis1)
summary(modelo6)
##
## Call:
## glm(formula = Y ~ FORMAJURIDICA, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4823 -1.3663 0.9005 0.9996 0.9996
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.43382 0.09146 4.743 2.1e-06 ***
## FORMAJURIDICA1 0.25932 0.17173 1.510 0.131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 944.97 on 713 degrees of freedom
## Residual deviance: 942.66 on 712 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 946.66
##
## Number of Fisher Scoring iterations: 4
modelo7=glm(formula =Y ~RUT, family = "binomial", data=datos_tesis1)
summary(modelo7)
##
## Call:
## glm(formula = Y ~ RUT, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5365 -1.2106 0.8567 0.8567 1.1446
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07778 0.11898 0.654 0.513
## RUT1 0.73566 0.15814 4.652 3.29e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 926.05 on 714 degrees of freedom
## AIC: 930.05
##
## Number of Fisher Scoring iterations: 4
modelo8=glm(formula =Y ~SOLVENCIA, family = "binomial", data=datos_tesis1)
summary(modelo8)
##
## Call:
## glm(formula = Y ~ SOLVENCIA, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.196 -1.334 1.028 1.028 1.028
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.36124 0.08048 4.489 7.16e-06 ***
## SOLVENCIA1 1.95552 0.40416 4.838 1.31e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 911.08 on 714 degrees of freedom
## AIC: 915.08
##
## Number of Fisher Scoring iterations: 4
modelo9=glm(formula =Y ~MORA, family = "binomial", data=datos_tesis1)
summary(modelo9)
##
## Call:
## glm(formula = Y ~ MORA, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9728 -1.3859 0.9823 0.9823 0.9823
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.47796 0.07804 6.124 9.1e-10 ***
## MORA1 1.31380 0.62847 2.090 0.0366 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 942.11 on 714 degrees of freedom
## AIC: 946.11
##
## Number of Fisher Scoring iterations: 4
modelo10a=glm(formula =Y ~+M1, family = "binomial", data=datos_tesis1)
summary(modelo10a)
##
## Call:
## glm(formula = Y ~ +M1, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2218 -1.2267 0.4208 1.1289 1.1289
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.11515 0.08629 1.335 0.182
## M11 2.26439 0.28335 7.992 1.33e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 848.16 on 714 degrees of freedom
## AIC: 852.16
##
## Number of Fisher Scoring iterations: 5
modelo10b=glm(formula =Y ~+M2, family = "binomial", data=datos_tesis1)
summary(modelo10b)
##
## Call:
## glm(formula = Y ~ +M2, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3357 -1.2462 0.3677 1.1102 1.1102
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16020 0.08456 1.894 0.0582 .
## M21 2.50006 0.33785 7.400 1.36e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 850.77 on 714 degrees of freedom
## AIC: 854.77
##
## Number of Fisher Scoring iterations: 5