Análise de Dados.

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.5     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
## Warning: package 'readr' was built under R version 4.1.3
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## 
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
## 
##     group_rows
## 
## Please cite as:
##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer

A tibble: 306 x 6

graduation pop_tot income_cat wage age coveq_pop_p 1 Yes 3070000 Lower middle income Yes 2 0 2 No 101700000 Lower middle income No 11 0 3 No NA Upper middle income Yes 1 NA 4 Yes 101700000 Lower middle income No 1 0 5 No 101700000 Lower middle income No 5 0 6 No 14780000 Lower middle income Yes 3 1 7 Yes NA Upper middle income No 3 NA 8 Yes 101700000 Lower middle income No 1 0 9 Yes 52400000 Lower middle income No 2 0 10 No 264000000 Lower middle income No 3 0 # … with 296 more rows

Statistic N Mean St. Dev. Min Max
pop_tot 199 114,043,997.000 267,455,098.000 40,654 1,247,000,000
age 219 3.813 4.885 0 38
coveq_pop_p 199 0.693 2.013 0 17
Statistic N Mean St. Dev. Min Max
pop_tot 199 114,043,997.000 267,455,098.000 40,654 1,247,000,000
age 219 3.813 4.885 0 38
coveq_pop_p 199 0.693 2.013 0 17
graduation_No 219 0.575 0.495 0 1
graduation_Yes 219 0.425 0.495 0 1
graduation_NA 306 0.284 0.452 0 1
income_cat_High income 219 0.032 0.176 0 1
income_cat_Low income 219 0.374 0.485 0 1
income_cat_Lower middle income 219 0.429 0.496 0 1
income_cat_Upper middle income 219 0.164 0.371 0 1
income_cat_NA 306 0.284 0.452 0 1
wage_No 219 0.653 0.477 0 1
wage_Yes 219 0.347 0.477 0 1
wage_NA 306 0.284 0.452 0 1

Modelar

Essa modelagem é básica, e não segue um padrão econométrico válido, serve apenas para intuir sobre a aplicação do modelo de MQO.

\(y_i= \beta_0 +\beta_1*AGE_i+\beta_2*G_N+\beta_3*I_l+\beta_4*Pop_{tot}\)

Call: lm(formula = coveq_pop_p ~ age + graduation_No + renda_baixa + pop_tot, data = dados_02)

Coefficients: (Intercept) age graduation_No renda_baixa pop_tot
2.883e-01 2.781e-03 6.745e-01 2.041e-01 -4.391e-10

Dependent variable:
coveq_pop_p
age 0.003
(0.028)
graduation_No 0.675**
(0.287)
renda_baixa 0.204
(0.301)
pop_tot -0.000
(0.000)
Constant 0.288
(0.290)
Observations 199
R2 0.033
Adjusted R2 0.013
Residual Std. Error 2.000 (df = 194)
F Statistic 1.661 (df = 4; 194)
Note: p<0.1; p<0.05; p<0.01

Resultados

Modelo pouco ajustado aos dados, com erros grandes em relação a modelagem.