Importacion 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
Estimando el modelo
library(stargazer)
modelo_1<-lm(formula = price~lotsize+sqrft+bdrms, data = hprice1)
stargazer(modelo_1, title = 'Modelo Estimado', type= 'text')
##
## Modelo Estimado
## ===============================================
## Dependent variable:
## ---------------------------
## price
## -----------------------------------------------
## lotsize 0.002***
## (0.001)
##
## sqrft 0.123***
## (0.013)
##
## bdrms 13.853
## (9.010)
##
## Constant -21.770
## (29.475)
##
## -----------------------------------------------
## Observations 88
## R2 0.672
## Adjusted R2 0.661
## Residual Std. Error 59.833 (df = 84)
## F Statistic 57.460*** (df = 3; 84)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Pruebas de normalidad de los residuos
library(fitdistrplus)
ajuste_normal<-fitdist(data = modelo_1$residuals, distr = 'norm')
plot(ajuste_normal)
summary(ajuste_normal)
## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 9.992007e-16 6.231624
## sd 5.845781e+01 4.406424
## Loglikelihood: -482.8775 AIC: 969.7549 BIC: 974.7096
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1
Prueba de Normalidad de Jarque - Bera
library(normtest)
jb.norm.test(modelo_1$residuals)
##
## Jarque-Bera test for normality
##
## data: modelo_1$residuals
## JB = 32.278, p-value = 0.002
qqnorm(modelo_1$residuals)
qqline(modelo_1$residuals)
library(fastGraph)
shadeDist(32.278,ddist = 'dchisq',parm1 = qchisq(0.01,2,lower.tail = FALSE),lower.tail = FALSE)
Prueba de Normalidad de Kolmogorov - Smirnov
library(nortest)
lillie.test(modelo_1$residuals)
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: modelo_1$residuals
## D = 0.075439, p-value = 0.2496
qqnorm(modelo_1$residuals)
qqline(modelo_1$residuals)
hist(modelo_1$residuals,main = "Histograma de los residuos",xlab = "Residuos",ylab = "frecuencia")
Prueba de Normalidad de Shapiro - Wilk
shapiro.test(modelo_1$residuals)
##
## Shapiro-Wilk normality test
##
## data: modelo_1$residuals
## W = 0.94132, p-value = 0.0005937
qqnorm(modelo_1$residuals)
qqline(modelo_1$residuals)
library(fastGraph)
shadeDist(0.94132,parm1 = 0,ddist ="dnorm",lower.tail = F)
