TallerFinal
Miguel Soto
2024-11-21
require(ggplot2)## Cargando paquete requerido: ggplot2require(datarium)## Cargando paquete requerido: datarium## Warning: package 'datarium' was built under R version 4.4.2require(plotly)## Cargando paquete requerido: plotly## Warning: package 'plotly' was built under R version 4.4.2##
## Adjuntando el paquete: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(readxl) Preparacion de los Datos
Se seleccionan 1030 de los 1471 datos, el resto se usaran para validar el modelo
dataFull <- read_excel("D:/Uni/PEBD/base3_ingreso.xlsx")
data <- head(dataFull, 1030)
dataVal <- tail(dataFull, 441)
income <- data$Ingreso_Mensual
data## # A tibble: 1,030 × 24
## Ingreso_Mensual Edad Educación Años_Experiencia Antigüedad Horas_Extra
## <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 5993000 41 2 8 6 Si
## 2 5130000 49 1 10 10 No
## 3 2090000 37 2 7 0 Si
## 4 2909000 33 4 8 8 Si
## 5 3468000 27 1 6 2 No
## 6 3068000 32 2 8 7 No
## 7 2670000 59 3 12 1 Si
## 8 2693000 30 1 1 1 No
## 9 9526000 38 3 10 9 No
## 10 5237000 36 3 17 7 No
## # ℹ 1,020 more rows
## # ℹ 18 more variables: Departamento <chr>, Distancia_Casa <dbl>,
## # Campo_Educación <chr>, Satisfacción_Ambiental <dbl>, Genero <chr>,
## # Cargo <chr>, Satisfación_Laboral <dbl>, Estado_Civil <chr>,
## # Trabajos_Anteriores <dbl>, Porcentaje_aumento_salarial <dbl>,
## # Rendimiento_Laboral <dbl>, Capacitaciones <dbl>,
## # Equilibrio_Trabajo_Vida <dbl>, Antigüedad_Cargo <dbl>, …Analisis Exploratorio
Promedio, Desviacion Estandar y Grafico:
avg=mean(income)
stdev=sd(income)
data.frame(avg, stdev)## avg stdev
## 1 6625619 4853762g1=ggplot(data = data,mapping = aes(x=Ingreso_Mensual))+geom_histogram(fill="blue")+theme_bw()
ggplotly(g1)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Analisis Bivariado
Años de Experiencia:
g2=ggplot(data=data,mapping = aes(x=Años_Experiencia,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_smooth()
ggplotly(g2)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'cor(data$Años_Experiencia, data$Ingreso_Mensual)## [1] 0.7801591Se observa una relacion positiva fuerte con esta variable
Antigüedad
g2=ggplot(data=data,mapping = aes(x=Antigüedad,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_smooth()
ggplotly(g2)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'cor(data$Antigüedad, data$Ingreso_Mensual)## [1] 0.5281034Se observa una relacion positiva media con esta variable
Porcentaje de Aumento
g2=ggplot(data=data,mapping = aes(x=Porcentaje_aumento_salarial,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_smooth()
ggplotly(g2)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'cor(data$Porcentaje_aumento_salarial, data$Ingreso_Mensual)## [1] -0.0584471No se observa una correlacion notoria para esta variable
Trabajos Anteriores
g2=ggplot(data=data,mapping = aes(x=Trabajos_Anteriores,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_smooth()
ggplotly(g2)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'cor(data$Trabajos_Anteriores, data$Ingreso_Mensual)## [1] 0.1485095Se observa una relacion positiva baja con esta variable
Antigüedad del Cargo
g2=ggplot(data=data,mapping = aes(x=Antigüedad_Cargo,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_smooth()
ggplotly(g2)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'cor(data$Antigüedad_Cargo, data$Ingreso_Mensual)## [1] 0.3822393Se observa una relacion positiva media con esta variable
Rendimiento Laboral
g2=ggplot(data=data,mapping = aes(x=Rendimiento_Laboral,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?cor(data$Rendimiento_Laboral, data$Ingreso_Mensual)## [1] -0.05661637No se observa una correlacion notoria para esta variable
Distancia a Casa
g2=ggplot(data=data,mapping = aes(x=Distancia_Casa,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_smooth()
ggplotly(g2)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'cor(data$Distancia_Casa, data$Ingreso_Mensual)## [1] -0.04767018No se observa una correlacion notoria para esta variable
Educacion
g2=ggplot(data=data,mapping = aes(x=Educación,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?cor(data$Educación, data$Ingreso_Mensual)## [1] 0.09612249Se observa una correlacion positiva baja para esta variable
Edad
g2=ggplot(data=data,mapping = aes(x=Edad,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_smooth()
ggplotly(g2)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'cor(data$Edad, data$Ingreso_Mensual)## [1] 0.5024369Se observa una relacion positiva media con esta variable
Horas Extra
g2=ggplot(data=data,mapping = aes(x=Horas_Extra,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)Se observa un promedio mayor para los datos con horas extra
Campo de Educacion
g2=ggplot(data=data,mapping = aes(x=Campo_Educación,y=Ingreso_Mensual))+geom_point()+theme_bw()+geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)Ordenados de menor a mayor de acuerdo al promedio, los campos serian Tecnicos, Salud, Ciencias, Otra, Mercadeo, Humanidades
Genero
g2=ggplot(data=data,mapping = aes(x=Genero,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)Se observa un promedio mayor para las mujeres
Cargo
g2=ggplot(data=data,mapping = aes(x=Cargo,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)Rotacion
g2=ggplot(data=data,mapping = aes(x=Rotación,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)Viajes
g2=ggplot(data=data,mapping = aes(x=`Viaje de Negocios`,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)Capacitaciones
g2=ggplot(data=data,mapping = aes(x=Capacitaciones,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?Departamento
g2=ggplot(data=data,mapping = aes(x=Departamento,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)Satisfaccion Ambiental
g2=ggplot(data=data,mapping = aes(x=Satisfacción_Ambiental,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?Satisfaccion Laboral
g2=ggplot(data=data,mapping = aes(x=Satisfación_Laboral,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?Años Ultima Promocion
g2=ggplot(data=data,mapping = aes(x=Años_ultima_promoción,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_smooth()
ggplotly(g2)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'cor(data$Años_ultima_promoción, data$Ingreso_Mensual)## [1] 0.3497339Años Acargo con Mismo Jefe
g2=ggplot(data=data,mapping = aes(x=Años_acargo_con_mismo_jefe,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_smooth()
ggplotly(g2)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'cor(data$Años_acargo_con_mismo_jefe, data$Ingreso_Mensual)## [1] 0.3429635Equilibrio Trabajo-Vida
g2=ggplot(data=data,mapping = aes(x=Equilibrio_Trabajo_Vida,y=Ingreso_Mensual))+geom_point()+theme_bw()+
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "tomato")
ggplotly(g2)## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?Modelos Simples
Se seleccionan las variables de experiencia, antiguedad y cargo ya que se espera que estas impacten la eficiencia y la importancia de un trabajador, lo cual se veria reflejado en el ingreso mensual
Modelo de experiencia
Estimacion del modelo:
modExp = lm(Ingreso_Mensual~Años_Experiencia, data=data)
summary(modExp)##
## Call:
## lm(formula = Ingreso_Mensual ~ Años_Experiencia, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11463736 -1802723 -131 1367389 11486290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1229552 164840 7.459 1.85e-13 ***
## Años_Experiencia 472816 11825 39.985 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3038000 on 1028 degrees of freedom
## Multiple R-squared: 0.6086, Adjusted R-squared: 0.6083
## F-statistic: 1599 on 1 and 1028 DF, p-value: < 2.2e-16Predicciones:
expPred <- predict(modExp,list(Años_Experiencia=dataVal$Años_Experiencia))
actualVals <- dataVal$Ingreso_Mensual
error <- actualVals - expPred
res <- data.frame(actualVals, expPred, error)
res## actualVals expPred error
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## 440 5390000 9267420 -3877420.2735
## 441 4404000 4066447 337553.4361MAE=mean(abs(error))
MAE## [1] 2150981Modelo de Antiguedad
modSen = lm(Ingreso_Mensual~Antigüedad, data=data)
summary(modSen)##
## Call:
## lm(formula = Ingreso_Mensual ~ Antigüedad, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9677242 -2542218 -1215631 1376539 15072034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3742635 193429 19.35 <2e-16 ***
## Antigüedad 406165 20370 19.94 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4124000 on 1028 degrees of freedom
## Multiple R-squared: 0.2789, Adjusted R-squared: 0.2782
## F-statistic: 397.6 on 1 and 1028 DF, p-value: < 2.2e-16senPred <- predict(modSen,list(Antigüedad=dataVal$Antigüedad))
actualVals <- dataVal$Ingreso_Mensual
error <- actualVals - senPred
res <- data.frame(actualVals, senPred, error)
res## actualVals senPred error
## 1 3975000 6991957 -3016956.746
## 2 10793000 9022783 1770217.375
## 3 10096000 6585792 3510208.430
## 4 3646000 4148801 -502800.515
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## 12 13757000 7398122 6358878.078
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## 439 6142000 6179626 -37626.394
## 440 5390000 7398122 -2008121.922
## 441 4404000 5367296 -963296.043MAE=mean(abs(error))
MAE## [1] 2865505Modelo del Cargo
modPos = lm(Ingreso_Mensual~Cargo, data=data)
summary(modPos)##
## Call:
## lm(formula = Ingreso_Mensual ~ Cargo, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5582582 -1136391 -306237 1194441 6943188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16132475 267860 60.227 < 2e-16 ***
## CargoDirector_Manofactura -8713046 334761 -26.028 < 2e-16 ***
## CargoEjecutivo_Ventas -9203663 301217 -30.555 < 2e-16 ***
## CargoGerente 1007108 354025 2.845 0.00453 **
## CargoInvestigador_Cientifico -12976598 303162 -42.804 < 2e-16 ***
## CargoRecursos_Humanos -11405760 472159 -24.157 < 2e-16 ***
## CargoRepresentante_Salud -8417557 348642 -24.144 < 2e-16 ***
## CargoRepresentante_Ventas -13527317 382120 -35.401 < 2e-16 ***
## CargoTecnico_Laboratorio -12898583 307824 -41.902 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2057000 on 1021 degrees of freedom
## Multiple R-squared: 0.8217, Adjusted R-squared: 0.8203
## F-statistic: 588.2 on 8 and 1021 DF, p-value: < 2.2e-16posPred <- predict(modPos,list(Cargo=dataVal$Cargo))
actualVals <- dataVal$Ingreso_Mensual
error <- actualVals - posPred
res <- data.frame(actualVals, posPred, error)
res## actualVals posPred error
## 1 3975000 3233891 741108.70
## 2 10793000 6928812 3864188.34
## 3 10096000 6928812 3167188.34
## 4 3646000 3233891 412108.70
## 5 7446000 7419429 26571.43
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## 13 8463000 6928812 1534188.34
## 14 3162000 3233891 -71891.30
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## 382 5677000 6928812 -1251811.66
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## 384 3748000 3233891 514108.70
## 385 3977000 3233891 743108.70
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## 396 5098000 3155876 1942123.81
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## 401 4108000 3155876 952123.81
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## 416 2339000 3233891 -894891.30
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## 422 8837000 4726714 4110285.71
## 423 5343000 6928812 -1585811.66
## 424 6728000 6928812 -200811.66
## 425 6652000 6928812 -276811.66
## 426 4850000 6928812 -2078811.66
## 427 2809000 3155876 -346876.19
## 428 5689000 7714918 -2025917.65
## 429 2001000 3155876 -1154876.19
## 430 2977000 3155876 -178876.19
## 431 4025000 3233891 791108.70
## 432 3785000 3155876 629123.81
## 433 10854000 6928812 3925188.34
## 434 12031000 6928812 5102188.34
## 435 9936000 7419429 2516571.43
## 436 2966000 2605158 360842.11
## 437 2571000 3233891 -662891.30
## 438 9991000 7714918 2276082.35
## 439 6142000 7419429 -1277428.57
## 440 5390000 6928812 -1538811.66
## 441 4404000 3233891 1170108.70MAE=mean(abs(error))
MAE## [1] 1560911Modelos Multiples
Modelo 1
Para el modelo 1 se selecciona el cargo y la antiguedad de este:
mod1 = lm(Ingreso_Mensual~Cargo+Antigüedad_Cargo, data=data)
summary(mod1)##
## Call:
## lm(formula = Ingreso_Mensual ~ Cargo + Antigüedad_Cargo, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5938077 -1175306 -291403 1071028 7218340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15328686 285219 53.744 < 2e-16 ***
## CargoDirector_Manofactura -8592975 327430 -26.244 < 2e-16 ***
## CargoEjecutivo_Ventas -9039034 295142 -30.626 < 2e-16 ***
## CargoGerente 930349 345976 2.689 0.00728 **
## CargoInvestigador_Cientifico -12590775 301098 -41.816 < 2e-16 ***
## CargoRecursos_Humanos -11130667 462833 -24.049 < 2e-16 ***
## CargoRepresentante_Salud -8253377 341337 -24.180 < 2e-16 ***
## CargoRepresentante_Ventas -12957956 381817 -33.938 < 2e-16 ***
## CargoTecnico_Laboratorio -12495639 306019 -40.833 < 2e-16 ***
## Antigüedad_Cargo 131004 18507 7.079 2.7e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2010000 on 1020 degrees of freedom
## Multiple R-squared: 0.8301, Adjusted R-squared: 0.8286
## F-statistic: 553.6 on 9 and 1020 DF, p-value: < 2.2e-16Seleccion de variables:
mod1Step = step(mod1)## Start: AIC=29907.76
## Ingreso_Mensual ~ Cargo + Antigüedad_Cargo
##
## Df Sum of Sq RSS AIC
## <none> 4.1197e+15 29908
## - Antigüedad_Cargo 1 2.0238e+14 4.3221e+15 29955
## - Cargo 8 1.6581e+16 2.0700e+16 31555summary(mod1Step)##
## Call:
## lm(formula = Ingreso_Mensual ~ Cargo + Antigüedad_Cargo, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5938077 -1175306 -291403 1071028 7218340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15328686 285219 53.744 < 2e-16 ***
## CargoDirector_Manofactura -8592975 327430 -26.244 < 2e-16 ***
## CargoEjecutivo_Ventas -9039034 295142 -30.626 < 2e-16 ***
## CargoGerente 930349 345976 2.689 0.00728 **
## CargoInvestigador_Cientifico -12590775 301098 -41.816 < 2e-16 ***
## CargoRecursos_Humanos -11130667 462833 -24.049 < 2e-16 ***
## CargoRepresentante_Salud -8253377 341337 -24.180 < 2e-16 ***
## CargoRepresentante_Ventas -12957956 381817 -33.938 < 2e-16 ***
## CargoTecnico_Laboratorio -12495639 306019 -40.833 < 2e-16 ***
## Antigüedad_Cargo 131004 18507 7.079 2.7e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2010000 on 1020 degrees of freedom
## Multiple R-squared: 0.8301, Adjusted R-squared: 0.8286
## F-statistic: 553.6 on 9 and 1020 DF, p-value: < 2.2e-16m1Pred <- predict(mod1,list(Cargo=dataVal$Cargo, Antigüedad_Cargo=dataVal$Antigüedad_Cargo))
actualVals <- dataVal$Ingreso_Mensual
error <- actualVals - m1Pred
res <- data.frame(actualVals, m1Pred, error)
res## actualVals m1Pred error
## 1 3975000 3750077 224923.35
## 2 10793000 7206682 3586318.27
## 3 10096000 7206682 2889318.27
## 4 3646000 2833047 812953.32
## 5 7446000 7783745 -337745.24
## 6 10851000 7992338 2858661.52
## 7 2109000 4460027 -2351027.00
## 8 3722000 3095055 626944.75
## 9 9380000 6866715 2513284.73
## 10 5486000 6551660 -1065660.32
## 11 2742000 4460027 -1718027.00
## 12 13757000 16376720 -2619719.85
## 13 8463000 6813669 1649331.12
## 14 3162000 3095055 66944.75
## 15 16598000 16376720 221280.15
## 16 6651000 7337317 -686317.07
## 17 2345000 2868914 -523914.43
## 18 3420000 2999919 420081.29
## 19 4373000 6682665 -2309664.60
## 20 4759000 7468690 -2709690.29
## 21 5301000 6420656 -1119656.04
## 22 3673000 4012085 -339085.21
## 23 4768000 6289652 -1521651.76
## 24 1274000 2737910 -1463910.15
## 25 4900000 3916949 983051.32
## 26 10466000 7992338 2473661.52
## 27 17007000 16376720 630280.15
## 28 2909000 2632738 276262.26
## 29 5765000 6682665 -917664.60
## 30 4599000 7468690 -2869690.29
## 31 2404000 2370729 33270.82
## 32 3172000 2833047 338953.32
## 33 2033000 2370729 -337729.18
## 34 10209000 6997720 3211280.45
## 35 8620000 7206682 1413318.27
## 36 2064000 4591031 -2527031.28
## 37 4035000 7337317 -3302317.07
## 38 3838000 3357064 480936.19
## 39 4591000 6813669 -2222668.88
## 40 2561000 2833047 -272046.68
## 41 1563000 2737910 -1174910.15
## 42 4898000 6551660 -1653660.32
## 43 4789000 3095055 1693944.75
## 44 3180000 3095055 84944.75
## 45 6549000 7521737 -972736.68
## 46 6388000 7075309 -687308.51
## 47 11244000 16521043 -5277043.08
## 48 16032000 17438073 -1406073.05
## 49 2362000 3654940 -1292940.12
## 50 16328000 16114711 213288.72
## 51 8376000 6735711 1640289.01
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## 53 8606000 8123343 482657.24
## 54 2272000 3226060 -954059.53
## 55 2018000 3357064 -1339063.81
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## 57 4084000 2999919 1084081.29
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## 59 2308000 3680772 -1372771.99
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## 61 4285000 3785944 499055.60
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## 65 10124000 8123343 2000657.24
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## 83 10169000 7652741 2516259.04
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## 85 4087000 3392932 694068.44
## 86 2367000 2999919 -632918.71
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## 90 2061000 2737910 -676910.15
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## 167 15379000 17176064 -1797064.48
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## 172 3195000 4460027 -1265027.00
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## 174 3306000 2833047 472953.32
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## 178 2570000 3750077 -1180076.65
## 179 3537000 2999919 537081.29
## 180 3986000 4143089 -157089.49
## 181 10883000 7075309 3807691.49
## 182 2028000 4274094 -2246093.77
## 183 9525000 6682665 2842335.40
## 184 2929000 3916949 -987948.68
## 185 2275000 2632738 -357737.74
## 186 7879000 7992338 -113338.48
## 187 4930000 3261927 1668072.72
## 188 7847000 7468690 378309.71
## 189 4401000 3130923 1270077.01
## 190 9241000 7337686 1903313.99
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## 193 10748000 9040373 1707627.28
## 194 1555000 4198018 -2643018.44
## 195 12936000 6944673 5991326.84
## 196 2305000 3095055 -790055.25
## 197 16704000 16114711 589288.72
## 198 3433000 2999919 433081.29
## 199 3477000 3095055 381944.75
## 200 6430000 4460027 1969973.00
## 201 6516000 6735711 -219710.99
## 202 3907000 3095055 811944.75
## 203 5562000 7992338 -2430338.48
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## 205 2862000 2737910 124089.85
## 206 4978000 6289652 -1311651.76
## 207 10368000 7075677 3292322.56
## 208 6134000 6551660 -417660.32
## 209 6735000 6289652 445348.24
## 210 3295000 3095055 199944.75
## 211 5238000 7259728 -2021728.11
## 212 6472000 3881081 2590919.07
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## 215 9756000 4198018 5557981.56
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## 237 3055000 3785944 -730944.40
## 238 2289000 3226060 -937059.53
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## 240 12965000 6997720 5967280.45
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## 242 6029000 6551660 -522660.32
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## 245 2398000 2833047 -435046.68
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## 267 10447000 8123712 2323288.31
## 268 9667000 7652741 2014259.04
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## 341 9854000 6289652 3564348.24
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## 432 3785000 3261927 523072.72
## 433 10854000 6551660 4302339.68
## 434 12031000 7468690 4562309.71
## 435 9936000 7259728 2676271.89
## 436 2966000 2632738 333262.26
## 437 2571000 3095055 -524055.25
## 438 9991000 7992338 1998661.52
## 439 6142000 6997720 -855719.55
## 440 5390000 7075677 -1685677.44
## 441 4404000 3226060 1177940.47MAE=mean(abs(error))
MAE## [1] 1488868Modelo 2
Para el modelo 2 se selecciona los años de experiencia y la edad
mod2 = lm(Ingreso_Mensual~Años_Experiencia+Edad, data=data)
summary(mod2)##
## Call:
## lm(formula = Ingreso_Mensual ~ Años_Experiencia + Edad, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11531483 -1699412 -12465 1386013 11381983
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2241885 417547 5.369 9.78e-08 ***
## Años_Experiencia 502586 16323 30.790 < 2e-16 ***
## Edad -36537 13853 -2.637 0.00848 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3029000 on 1027 degrees of freedom
## Multiple R-squared: 0.6113, Adjusted R-squared: 0.6105
## F-statistic: 807.5 on 2 and 1027 DF, p-value: < 2.2e-16Seleccion de variables:
mod2Step = step(mod2)## Start: AIC=30746
## Ingreso_Mensual ~ Años_Experiencia + Edad
##
## Df Sum of Sq RSS AIC
## <none> 9.4234e+15 30746
## - Edad 1 6.3825e+13 9.4872e+15 30751
## - Años_Experiencia 1 8.6990e+15 1.8122e+16 31418summary(mod2Step)##
## Call:
## lm(formula = Ingreso_Mensual ~ Años_Experiencia + Edad, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11531483 -1699412 -12465 1386013 11381983
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2241885 417547 5.369 9.78e-08 ***
## Años_Experiencia 502586 16323 30.790 < 2e-16 ***
## Edad -36537 13853 -2.637 0.00848 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3029000 on 1027 degrees of freedom
## Multiple R-squared: 0.6113, Adjusted R-squared: 0.6105
## F-statistic: 807.5 on 2 and 1027 DF, p-value: < 2.2e-16m2Pred <- predict(mod2,list(Años_Experiencia=dataVal$Años_Experiencia, Educación=dataVal$Educación, Edad = dataVal$Edad))
actualVals <- dataVal$Ingreso_Mensual
error <- actualVals - m2Pred
res <- data.frame(actualVals, m2Pred, error)
res## actualVals m2Pred error
## 1 3975000 6308843 -2333842.994
## 2 10793000 7642850 3150150.412
## 3 10096000 14633581 -4537581.105
## 4 3646000 6345380 -2699380.181
## 5 7446000 6135092 1310908.277
## 6 10851000 12659774 -1808774.472
## 7 2109000 5129920 -3020919.814
## 8 3722000 4627334 -905333.859
## 9 9380000 5623571 3756428.896
## 10 5486000 8026889 -2540889.317
## 11 2742000 2004793 737207.477
## 12 13757000 8821773 4935227.231
## 13 8463000 4234359 4228640.535
## 14 3162000 4152350 -990350.426
## 15 16598000 17895922 -1297922.479
## 16 6651000 10503282 -3852281.904
## 17 2345000 4801085 -2456085.130
## 18 3420000 3649764 -229764.471
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## 20 4759000 8538410 -3779409.937
## 21 5301000 3156113 2144886.819
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## 440 5390000 8995524 -3605524.040
## 441 4404000 4015136 388863.658MAE=mean(abs(error))
MAE## [1] 2152175Validacion de Supuestos
De acuerdo a los resultados anteriores, se usara el modelo 1 de aqui en adelante
par(mfrow=c(2,2))
plot(mod1)
Los graficos muestran que existe una relacion lineal, por lo que no es necesario realizar una transformación en el modelo
Conclusiones
Segun el analisis, se concluye que el modelo 1, usando el cargo y la antiguedad en este genera el pronostico mas acertado para el ingreso mensual de un empleado, no se encontro un mejor modelo con seleccion de variables o transformaciones
Pronostico Hipotetico:
hypPred <- predict(mod1,list(Cargo="Investigador_Cientifico", Antigüedad_Cargo=20))
hypPred## 1
## 5357996En la practica, este modelo de prediccion podria ser utilizado por un empleado para determinar si sus ingresos son acordes a su situación, o por el empleador para determinar el valor de un contrato.