NICOLE SARAI AGUILAR HERNANDEZ
Ejercicio de pruebas de Normalidad
2023-05-07
Importación de datos.
library(wooldridge)## Warning: package 'wooldridge' was built under R version 4.2.3data(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 7.829630Estimación del modelo.
library(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazermod_estimado<-lm("price~lotsize+sqrft+bdrms", data = hprice1)
stargazer(mod_estimado,
title = "Modelo para ejercicio de prueba de normalidad",
type = "html")##
## <table style="text-align:center"><caption><strong>Modelo para ejercicio de prueba de normalidad</strong></caption>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>NA</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">lotsize</td><td>0.002<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">sqrft</td><td>0.123<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.013)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">bdrms</td><td>13.853</td></tr>
## <tr><td style="text-align:left"></td><td>(9.010)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-21.770</td></tr>
## <tr><td style="text-align:left"></td><td>(29.475)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>88</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.672</td></tr>
## <tr><td style="text-align:left">Adjusted R<sup>2</sup></td><td>0.661</td></tr>
## <tr><td style="text-align:left">Residual Std. Error</td><td>59.833 (df = 84)</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>57.460<sup>***</sup> (df = 3; 84)</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>#Ajuste de los residuos
library(fitdistrplus)## Warning: package 'fitdistrplus' was built under R version 4.2.3## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:wooldridge':
##
## cement## Loading required package: survivalfit_normal<-fitdist(data = mod_estimado$residuals,distr = "norm")
plot(fit_normal)
summary(fit_normal)## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 9.992007e-16 6.231624
## sd 5.845781e+01 4.406424
## Loglikelihood: -482.8775 AIC: 969.7549 BIC: 974.7096
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1Verificación del supesto de normalidad a traves de:
Prueba de JB
library(tseries)## Warning: package 'tseries' was built under R version 4.2.3## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoosalida_JB<-jarque.bera.test(mod_estimado$residuals)
salida_JB##
## Jarque Bera Test
##
## data: mod_estimado$residuals
## X-squared = 32.278, df = 2, p-value = 9.794e-08De forma gráfica
library(fastGraph)## Warning: package 'fastGraph' was built under R version 4.2.3sig<-0.05
JB<-salida_JB$statistic
gl<-salida_JB$parameter
VC<-qchisq(1-sig,
gl,
lower.tail = TRUE)
shadeDist(JB,ddist = "dchisq",
parm1 = gl,
lower.tail = FALSE,
xmin=0,
sub=paste("VC:",round(VC,2)," ","JB:",round(JB,2)))
Prueba de KS
library(nortest)
prueba_KS<-lillie.test(mod_estimado$residuals)
prueba_KS##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: mod_estimado$residuals
## D = 0.075439, p-value = 0.2496De forma tabular
tabla_KS<-data.frame(distribucion = "Normal",
Estadistico = prueba_KS$statistic,
Valr_p = prueba_KS$p.value)
summary(tabla_KS)## distribucion Estadistico Valr_p
## Length:1 Min. :0.07544 Min. :0.2496
## Class :character 1st Qu.:0.07544 1st Qu.:0.2496
## Mode :character Median :0.07544 Median :0.2496
## Mean :0.07544 Mean :0.2496
## 3rd Qu.:0.07544 3rd Qu.:0.2496
## Max. :0.07544 Max. :0.2496Pueba de SW
salida_SW<-shapiro.test(mod_estimado$residuals)
print(salida_SW)##
## Shapiro-Wilk normality test
##
## data: mod_estimado$residuals
## W = 0.94132, p-value = 0.0005937# Wn
Wn_salida<-qnorm(salida_SW$p.value,lower.tail = FALSE)
print(Wn_salida)## [1] 3.241867De forma tabular
tabla_SW<-data.frame(Estadistico= salida_SW$statistic,
Valor_p = salida_SW$p.value,
Metodo = "shapro-Wlk")
summary(tabla_SW)## Estadistico Valor_p Metodo
## Min. :0.9413 Min. :0.0005937 Length:1
## 1st Qu.:0.9413 1st Qu.:0.0005937 Class :character
## Median :0.9413 Median :0.0005937 Mode :character
## Mean :0.9413 Mean :0.0005937
## 3rd Qu.:0.9413 3rd Qu.:0.0005937
## Max. :0.9413 Max. :0.0005937De forma gráfica
Wn_salida<-qnorm(salida_SW$p.value,
lower.tail = FALSE)
print(Wn_salida)## [1] 3.241867library(fastGraph)
shadeDist(Wn_salida, ddist = "dnorm",lower.tail = FALSE)