#informe

#anexo

#modelos

summary(modelo0)
## 
## Call:
## lm(formula = y ~ 1, data = base)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34415  -8415    836   3058  38585 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   293315       1341   218.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13340 on 98 degrees of freedom
summary(modelo1)
## 
## Call:
## lm(formula = y ~ x1 + x2 + x3 + x4 + x5, data = base)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -14660  -4197   1308   4161  16174 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.134e+05  5.470e+03  39.018  < 2e-16 ***
## x1          3.482e+01  3.631e+00   9.590  1.5e-15 ***
## x2          1.531e+02  1.958e+03   0.078  0.93784    
## x3          8.616e+02  3.208e+02   2.685  0.00858 ** 
## x4          1.650e+04  1.581e+03  10.440  < 2e-16 ***
## x5          1.751e+03  1.704e+03   1.028  0.30678    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6572 on 93 degrees of freedom
## Multiple R-squared:  0.7697, Adjusted R-squared:  0.7573 
## F-statistic: 62.16 on 5 and 93 DF,  p-value: < 2.2e-16
summary(modelo2)
## 
## Call:
## lm(formula = y ~ x1 + x3 + x4, data = BienesyCasas1)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -13490  -4597   1323   3699  17295 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.133e+05  5.275e+03  40.429  < 2e-16 ***
## x1          3.522e+01  2.541e+00  13.863  < 2e-16 ***
## x3          9.234e+02  3.133e+02   2.948  0.00403 ** 
## x4          1.596e+04  1.472e+03  10.841  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6540 on 95 degrees of freedom
## Multiple R-squared:  0.7671, Adjusted R-squared:  0.7597 
## F-statistic: 104.3 on 3 and 95 DF,  p-value: < 2.2e-16
summary(modelo3)
## 
## Call:
## lm(formula = log(y) ~ log(x1) + x3 + x4, data = base)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.049392 -0.014766  0.006685  0.012315  0.057834 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.868630   0.120769  89.995  < 2e-16 ***
## log(x1)      0.222328   0.016065  13.839  < 2e-16 ***
## x3           0.002943   0.001064   2.765  0.00685 ** 
## x4           0.054277   0.004993  10.871  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02216 on 95 degrees of freedom
## Multiple R-squared:  0.7651, Adjusted R-squared:  0.7577 
## F-statistic: 103.2 on 3 and 95 DF,  p-value: < 2.2e-16
summary(modelo2)
## 
## Call:
## lm(formula = y ~ x1 + x3 + x4, data = BienesyCasas1)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -13490  -4597   1323   3699  17295 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.133e+05  5.275e+03  40.429  < 2e-16 ***
## x1          3.522e+01  2.541e+00  13.863  < 2e-16 ***
## x3          9.234e+02  3.133e+02   2.948  0.00403 ** 
## x4          1.596e+04  1.472e+03  10.841  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6540 on 95 degrees of freedom
## Multiple R-squared:  0.7671, Adjusted R-squared:  0.7597 
## F-statistic: 104.3 on 3 and 95 DF,  p-value: < 2.2e-16
plot(modelo2)

modelo2 <- lm(y ~ x1 + x3 + x4, data = BienesyCasas1)
# Crear las casas con las características deseadas
casa1 <- c(2190, 3, 4, 1, 0)
casa2 <- c(1848, 3, 9, 0, 1)
casas <- rbind.data.frame(casa1, casa2)

# Obtener los nombres de las columnas del dataframe base excluyendo la primera columna (que generalmente es el ID)
nombres_columnas <- names(base)[-1]  # Excluimos la primera columna

# Asignar los nombres de las columnas al dataframe de casas
colnames(casas) <- nombres_columnas

# Realizar la predicción con el modelo
predicciones <- predict(modelo2, newdata = casas, level = 0.95, interval = "confidence") * 1000
predicciones
##         fit       lwr       upr
## 1 310053682 306680559 313426805
## 2 286660747 284713359 288608135

Fue muy mal vendida porque la estimacion da un valor inferior a la cual fue vendida.