Datos utilizados

library(wooldridge)
data(hprice1)
head(force(hprice1),n=5)#mostrar primeras 5 observaciones 
##   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

Estimar modelo

library(stargazer)
modelo_lineal<-lm(formula = price~lotsize+sqrft+bdrms,data = hprice1)
stargazer(modelo_lineal,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

A)Prueba de White

options(scipen = 9999)
library(lmtest)
prueba_white<-bptest(modelo_lineal,~I(lotsize^2)+I(sqrft^2)+I(bdrms^2)+lotsize*sqrft+lotsize*bdrms+sqrft*bdrms,data = hprice1)
print(prueba_white)
## 
##  studentized Breusch-Pagan test
## 
## data:  modelo_lineal
## BP = 33.732, df = 9, p-value = 0.00009953

B)Grafica

library(fastGraph)
## Warning: package 'fastGraph' was built under R version 4.5.3
alpha_sig<-0.05
white<-prueba_white$statistic
gl<-prueba_white$parameter
shadeDist(white,ddist = "dchisq",parm1 = gl,lower.tail = FALSE)

Como 0.00009953<0.05 Se rechaza la H0, por lo tanto la varianza de los residuos presenta heterocedasticidad