Prueba de Normalidad
1. Carga de Datos
library(wooldridge)
data(hprice1)
head(force(hprice1),n=5)
## price assess bdrms lotsize sqrft colonial lprice lassess llotsize lsqrft
## 1 300 349.1 4 6126 2438 1 5.703783 5.855359 8.720297 7.798934
## 2 370 351.5 3 9903 2076 1 5.913503 5.862210 9.200593 7.638198
## 3 191 217.7 3 5200 1374 0 5.252274 5.383118 8.556414 7.225482
## 4 195 231.8 3 4600 1448 1 5.273000 5.445875 8.433811 7.277938
## 5 373 319.1 4 6095 2514 1 5.921578 5.765504 8.715224 7.829630
1. Estimacion del Modelo
modelo_hprice1<-lm(formula = price~ (lotsize) + (sqrft) + (bdrms) , data = hprice1)
library(stargazer)
stargazer(modelo_hprice1, title = "hprice1", type = "text", digits = 8)
##
## hprice1
## ===============================================
## Dependent variable:
## ---------------------------
## price
## -----------------------------------------------
## lotsize 0.00206771***
## (0.00064213)
##
## sqrft 0.12277820***
## (0.01323741)
##
## bdrms 13.85252000
## (9.01014500)
##
## Constant -21.77031000
## (29.47504000)
##
## -----------------------------------------------
## Observations 88
## R2 0.67236220
## Adjusted R2 0.66066090
## Residual Std. Error 59.83348000 (df = 84)
## F Statistic 57.46023000*** (df = 3; 84)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
2.Verique el supuesto de normalidad, a través de:
a) La prueba JB
library(normtest)
jb.norm.test(modelo_hprice1$residuals)
##
## Jarque-Bera test for normality
##
## data: modelo_hprice1$residuals
## JB = 32.278, p-value = 0.001
qqnorm(modelo_hprice1$residuals)
qqline(modelo_hprice1$residuals)
hist(modelo_hprice1$residuals,main = "Histograma de los residuos",xlab = "Residuos",ylab = "frecuencia")
Libreria fastGraph
options(scipen = 99999)
Coef_modelo<-summary(modelo_hprice1)$coefficients
t_values<-Coef_modelo[,"t value"]
etiquetas<-names(t_values)
#Gráficas Pruebas t
for(j in 2:3){
tc<-t_values[j]
t_VC<-
fastGraph:: shadeDist( c(-tc, tc ), "dt", 13,col=c("black","red"),sub=paste("Parámetro de la Variable:",etiquetas[j]))
print(confint(modelo_hprice1,parm = j,level = 0.95))}
## 2.5 % 97.5 %
## lotsize 0.000790769 0.003344644
## 2.5 % 97.5 %
## sqrft 0.09645415 0.1491022
b) Prueba KS
library(nortest)
lillie.test(modelo_hprice1$residuals)
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: modelo_hprice1$residuals
## D = 0.075439, p-value = 0.2496
qqnorm(modelo_hprice1$residuals)
qqline(modelo_hprice1$residuals)
hist(modelo_hprice1$residuals,main = "Histograma de los residuos",xlab = "Residuos",ylab = "frecuencia")
c) La prueba SW
shapiro.test(modelo_hprice1$residuals)
##
## Shapiro-Wilk normality test
##
## data: modelo_hprice1$residuals
## W = 0.94132, p-value = 0.0005937
qqnorm(modelo_hprice1$residuals)
qqline(modelo_hprice1$residuals)
hist(modelo_hprice1$residuals,main = "Histograma de los residuos",xlab = "Residuos",ylab = "frecuencia")
Libreria fastGraph
options(scipen = 99999)
Coef_modelo<-summary(modelo_hprice1)$coefficients
t_values<-Coef_modelo[,"t value"]
etiquetas<-names(t_values)
#Gráficas Pruebas t
for(j in 2:3){
tc<-t_values[j]
t_VC<-
fastGraph:: shadeDist( c(-tc, tc ), "dt", 13,col=c("black","red"),sub=paste("Parámetro de la Variable:",etiquetas[j]))
print(confint(modelo_hprice1,parm = j,level = 0.95))}
## 2.5 % 97.5 %
## lotsize 0.000790769 0.003344644
## 2.5 % 97.5 %
## sqrft 0.09645415 0.1491022