Cargar librerías

library(readr) # Para importar datos
library(dplyr) # Para filtrar   
library(knitr) # Para datos tabulares
library(ggplot2) # Para visualizar
library(plotly)
library(caret)  # Para particionar
library(Metrics) # Para determinar rmse

Cargar datos

datos <- read.csv("https://raw.githubusercontent.com/fhernanb/Python-para-estadistica/master/03%20Regression/Regresi%C3%B3n%20lineal%20m%C3%BAltiple/softdrink.csv")

Crear el modelo

modelo.rm <- lm(data = datos, formula = y ~ x1 + x2)
modelo.rm
## 
## Call:
## lm(formula = y ~ x1 + x2, data = datos)
## 
## Coefficients:
## (Intercept)           x1           x2  
##     2.34123      1.61591      0.01438

summary modelo

summary(modelo.rm)
## 
## Call:
## lm(formula = y ~ x1 + x2, data = datos)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7880 -0.6629  0.4364  1.1566  7.4197 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.341231   1.096730   2.135 0.044170 *  
## x1          1.615907   0.170735   9.464 3.25e-09 ***
## x2          0.014385   0.003613   3.981 0.000631 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.259 on 22 degrees of freedom
## Multiple R-squared:  0.9596, Adjusted R-squared:  0.9559 
## F-statistic: 261.2 on 2 and 22 DF,  p-value: 4.687e-16

Referencias bibligráficas

https://yuasaavedraco.github.io/Docs/Regresi%C3%B3n_Lineal_M%C3%BAltiple_con_Python.html