library(readxl)
dataPeru <- read_excel("dataPeru.xlsx")
str(dataPeru)
## tibble [25 × 8] (S3: tbl_df/tbl/data.frame)
## $ DEPARTAMENTO : chr [1:25] "AMAZONAS" "ÁNCASH" "APURÍMAC" "AREQUIPA" ...
## $ UBIGEO : chr [1:25] "010000" "020000" "030000" "040000" ...
## $ buenEstado : num [1:25] 18.6 13.9 8.7 27.4 17 18 33.8 11.9 10.1 15.6 ...
## $ contribuyentesSunat: num [1:25] 75035 302906 103981 585628 151191 ...
## $ peaOcupada : num [1:25] 130019 387976 140341 645001 235857 ...
## $ pobUrbana : num [1:25] 205976 806065 243354 1383694 444473 ...
## $ PobRural : num [1:25] 211389 333050 180905 76739 206467 ...
## $ pobTotal : num [1:25] 417365 1139115 424259 1460433 650940 ...
modelo1=formula(buenEstado~contribuyentesSunat+peaOcupada)
reg1=lm(modelo1,data=dataPeru)
summary(reg1)
##
## Call:
## lm(formula = modelo1, data = dataPeru)
##
## 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) 1.865e+01 2.694e+00 6.922 5.98e-07 ***
## contribuyentesSunat 1.786e-05 2.060e-05 0.867 0.395
## peaOcupada -1.596e-05 2.241e-05 -0.712 0.484
## ---
## 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
library(modelsummary)
## Warning: package 'modelsummary' was built under R version 4.3.3
## `modelsummary` 2.0.0 now uses `tinytable` as its default table-drawing
## backend. Learn more at: https://vincentarelbundock.github.io/tinytable/
##
## Revert to `kableExtra` for one session:
##
## options(modelsummary_factory_default = 'kableExtra')
##
## Change the default backend persistently:
##
## config_modelsummary(factory_default = 'gt')
##
## Silence this message forever:
##
## config_modelsummary(startup_message = FALSE)
model1=list('Locales BE (I)'=reg1)
modelsummary(model1, title = "Regresion: modelo 1",
stars = TRUE,
output = "kableExtra")
Regresion: modelo 1
|
|
Locales BE (I)
|
|
(Intercept)
|
18.646***
|
|
|
(2.694)
|
|
contribuyentesSunat
|
0.000
|
|
|
(0.000)
|
|
peaOcupada
|
0.000
|
|
|
(0.000)
|
|
Num.Obs.
|
25
|
|
R2
|
0.156
|
|
R2 Adj.
|
0.079
|
|
AIC
|
179.3
|
|
BIC
|
184.1
|
|
Log.Lik.
|
-85.626
|
|
F
|
2.035
|
|
RMSE
|
7.43
|
|
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p <
0.001
|
Pregunta 2:
h1=formula(peaOcupada~contribuyentesSunat+buenEstado)
rl1=lm(h1, data = dataPeru)
model1=list('PEA ocupada (I)'=rl1)
modelsummary(model1, title = "Resumen de Regresion Lineal",
stars = TRUE,
output = "kableExtra")
Resumen de Regresion Lineal
|
|
PEA ocupada (I)
|
|
(Intercept)
|
115468.390**
|
|
|
(37869.931)
|
|
contribuyentesSunat
|
0.921***
|
|
|
(0.017)
|
|
buenEstado
|
-1411.649
|
|
|
(1982.682)
|
|
Num.Obs.
|
25
|
|
R2
|
0.993
|
|
R2 Adj.
|
0.993
|
|
AIC
|
636.7
|
|
BIC
|
641.6
|
|
Log.Lik.
|
-314.354
|
|
F
|
1603.130
|
|
RMSE
|
69927.80
|
|
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p <
0.001
|