library(rio)
library(tidyverse)
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## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
data=import("dataPeru.xlsx")
str(data)
## 'data.frame':    25 obs. of  8 variables:
##  $ DEPARTAMENTO       : chr  "AMAZONAS" "ÁNCASH" "APURÍMAC" "AREQUIPA" ...
##  $ UBIGEO             : chr  "010000" "020000" "030000" "040000" ...
##  $ buenEstado         : num  18.6 13.9 8.7 27.4 17 18 33.8 11.9 10.1 15.6 ...
##  $ contribuyentesSunat: num  75035 302906 103981 585628 151191 ...
##  $ peaOcupada         : num  130019 387976 140341 645001 235857 ...
##  $ pobUrbana          : num  205976 806065 243354 1383694 444473 ...
##  $ PobRural           : num  211389 333050 180905 76739 206467 ...
##  $ pobTotal           : num  417365 1139115 424259 1460433 650940 ...
peaOcupada2 = prop.table(data$peaOcupada)
contribuyentesSunat2 = prop.table(data$contribuyentesSunat)

#Pregunta 1

modelo1=formula(buenEstado~peaOcupada2 + contribuyentesSunat2)
regre1=lm(modelo1, data= data)
summary(regre1)
## 
## Call:
## lm(formula = modelo1, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.589  -3.966  -1.347   1.907  21.518 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            18.646      2.694   6.922 5.98e-07 ***
## peaOcupada2          -196.341    275.764  -0.712    0.484    
## contribuyentesSunat2  195.495    225.457   0.867    0.395    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.925 on 22 degrees of freedom
## Multiple R-squared:  0.1561, Adjusted R-squared:  0.07939 
## F-statistic: 2.035 on 2 and 22 DF,  p-value: 0.1546

#Pregunta 2

modelo2=formula(peaOcupada ~contribuyentesSunat + buenEstado)
regre2=lm(modelo2,data = data)
summary(regre2)
## 
## Call:
## lm(formula = modelo2, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -91867 -58573 -11166  46174 155851 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          1.155e+05  3.787e+04   3.049  0.00588 ** 
## contribuyentesSunat  9.206e-01  1.741e-02  52.872  < 2e-16 ***
## buenEstado          -1.412e+03  1.983e+03  -0.712  0.48395    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 74540 on 22 degrees of freedom
## Multiple R-squared:  0.9932, Adjusted R-squared:  0.9926 
## F-statistic:  1603 on 2 and 22 DF,  p-value: < 2.2e-16