library(stargazer)
library(readr)
ejemplo_regresion <- read_csv("C:/doc R/ejemplo_regresion.csv")
#corriendo el modelo de regresion
regresion<-lm(formula = Y~X1+X2,data = ejemplo_regresion)
stargazer(regresion,title = "modelo estimado",type = "text")
##
## modelo estimado
## ===============================================
## Dependent variable:
## ---------------------------
## Y
## -----------------------------------------------
## X1 0.237***
## (0.056)
##
## X2 -0.0002***
## (0.00003)
##
## Constant 1.564***
## (0.079)
##
## -----------------------------------------------
## Observations 25
## R2 0.865
## Adjusted R2 0.853
## Residual Std. Error 0.053 (df = 22)
## F Statistic 70.661*** (df = 2; 22)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
pruebas de normalidad de los rsiduos
library(fitdistrplus)
ajuste_normal<-fitdist(data = regresion$residuals,distr = "norm")
plot(ajuste_normal)
prueba de JB
library(normtest)
jb.norm.test(regresion$residuals)
##
## Jarque-Bera test for normality
##
## data: regresion$residuals
## JB = 0.93032, p-value = 0.4705
prueba KS (lillifomrs)
library(nortest)
lillie.test(regresion$residuals)
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: regresion$residuals
## D = 0.082345, p-value = 0.9328
prueba de shapiro
shapiro.test(regresion$residuals)
##
## Shapiro-Wilk normality test
##
## data: regresion$residuals
## W = 0.97001, p-value = 0.6453