setwd("C:/Users/julip/Downloads")
library(rio)
data=import("dataPeru.xlsx")#Al querer probar la hipotesis que el buen estado de los locales escolares depende del porcentaje de la poblacion que contribuye a la SUNAT ; y del porcentaje de la PEA que está laborando ; se llega a comprobar que (con una significancia del 0.05):
# hipotesis en R
modelo1=formula(buenEstado~contribuyentesSunat+peaOcupada)reg1=lm(modelo1,data=data)
summary(reg1)##
## 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) 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#Al querer probar la hipotesis que la cantidad de PEA ocupada dependen de la cantidad de contribuyentes a la SUNAT ; y del porcentaje de locales escolares en buen estado; se llega a comprobar que (con una significancia del 0.05):
library(knitr)
library(modelsummary)## `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')
## options(modelsummary_factory_latex = 'kableExtra')
## options(modelsummary_factory_html = 'kableExtra')
##
## Silence this message forever:
##
## config_modelsummary(startup_message = FALSE)h1=formula(peaOcupada~contribuyentesSunat+buenEstado)
rl1=lm(h1, data = data)
model1=list('OLS asegurados (I)'=rl1)
modelsummary(model1, title = "Resumen de Regresion Lineal",
stars = TRUE,
output = "kableExtra")| OLS asegurados (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 |
rps1=glm(rl1, data = data,
family = poisson(link = "log"))
# displaying results
modelslmpoi=list('OLS asegurados (II)'=rl1,
'POISSON asegurados'=rps1)
modelsummary(modelslmpoi, title = "Regresiones OLS y Poisson",
stars = TRUE,
output = "kableExtra")| OLS asegurados (II) | POISSON asegurados | |
|---|---|---|
| (Intercept) | 115468.390** | 12.380*** |
| (37869.931) | (0.001) | |
| contribuyentesSunat | 0.921*** | 0.000*** |
| (0.017) | (0.000) | |
| buenEstado | -1411.649 | 0.008*** |
| (1982.682) | (0.000) | |
| Num.Obs. | 25 | 25 |
| R2 | 0.993 | |
| R2 Adj. | 0.993 | |
| AIC | 636.7 | 2148901.3 |
| BIC | 641.6 | 2148904.9 |
| Log.Lik. | -314.354 | -1074447.636 |
| F | 1603.130 | 10413966.319 |
| RMSE | 69927.80 | 163155.83 |
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 |