Analisis de Regresion-variables indicadoras

datos_1<-mtcars[, c(1,6,8)] 
datos_1$vs<- as.factor(datos_1$vs)
str(datos_1)

## 'data.frame':    32 obs. of  3 variables:
##  $ mpg: num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ wt : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ vs : Factor w/ 2 levels "0","1": 1 1 2 2 1 2 1 2 2 2 ...

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.2

ggplot(data = datos_1, aes(x = wt, y = mpg, color = vs)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE)

## `geom_smooth()` using formula = 'y ~ x'

modelo3<-lm(mpg~wt+vs,data=datos_1)
summary(modelo3)

## 
## Call:
## lm(formula = mpg ~ wt + vs, data = datos_1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7071 -2.4415 -0.3129  1.4319  6.0156 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  33.0042     2.3554  14.012 1.92e-14 ***
## wt           -4.4428     0.6134  -7.243 5.63e-08 ***
## vs1           3.1544     1.1907   2.649   0.0129 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.78 on 29 degrees of freedom
## Multiple R-squared:  0.801,  Adjusted R-squared:  0.7873 
## F-statistic: 58.36 on 2 and 29 DF,  p-value: 6.818e-11