R Notebook

Zuriel Zamudio Paredes

library(pacman)
library(ggplot2)
library(readxl)
library(openxlsx)
library(magrittr)

Vamos a determinar los estimadores, realizando sumatorias El modelo es \(\hat y=b_1+ b_2pb+ b_3i\) *Para obtener \(b_1\), tenemos qye \(b_1= \bar q-b_2\bar pb - b_3\bar i\)

beer <- read_excel("beer.xlsx")
attach(beer)
names(beer)

## [1] "q"  "pb" "pl" "pr" "i"

#para b2
si2 <- sum(i ^ 2)
spb2 <- sum(pb ^ 2)

sipb <- sum(i * pb)
siq <- sum(i * q)
spbq <- sum(pb * q)
sipb2 <- sipb ^ 2

#medias
mq <- mean(q)
mi <- mean(i)
mpb <- mean(pb)

b2 <- ((spbq * si2) - (siq * sipb)) / ((spb2 * si2) - (sipb2))

b3 <- ((siq * spb2) - (spbq * sipb)) / ((spb2 * si2) - (sipb2))

b1 <- mq - (b2 * mpb) - (b3 * mi)

mod1 <- lm(q ~ pb + i, data = beer)
mod1

## 
## Call:
## lm(formula = q ~ pb + i, data = beer)
## 
## Coefficients:
## (Intercept)           pb            i  
##    57.15986    -27.65270      0.00258