Parcial

1. Instalar y correr librerias

library(fueleconomy)
library(dplyr)
n_lista <- 18
set.seed(n_lista)
vehicles %>% filter(year>=1990 & year<=1999) %>% sample_n(1000,replace = FALSE) -> data_1
vehicles %>% filter(year>=2000 & year<=2009) %>% sample_n(1000,replace = FALSE) -> data_2
vehicles %>% filter(year>=2010 & year<=2015) %>% sample_n(1000,replace = FALSE) -> data_3

2. Correr modelo que explique rendimiento promedio de los automaviles.

vehicles %>% select("cyl", "displ", "hwy", "cty") -> datostotales
stargazer::stargazer(datostotales, title = "rendimiento", type =  "text", digits =  8)

## 
## rendimiento
## ===================================================
## Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max
## ===================================================

4. generar matrices A, M y P para el dataframe data_3

MatrizA