library(rio)data=import("dataPeru.xlsx")
data## DEPARTAMENTO UBIGEO buenEstado contribuyentesSunat peaOcupada pobUrbana
## 1 AMAZONAS 010000 18.6 75035 130019 205976
## 2 ÁNCASH 020000 13.9 302906 387976 806065
## 3 APURÍMAC 030000 8.7 103981 140341 243354
## 4 AREQUIPA 040000 27.4 585628 645001 1383694
## 5 AYACUCHO 050000 17.0 151191 235857 444473
## 6 CAJAMARCA 060000 18.0 277457 461312 567141
## 7 CALLAO 070000 33.8 499257 445072 1046953
## 8 CUSCO 080000 11.9 466883 496399 865771
## 9 HUANCAVELICA 090000 10.1 80353 111996 166163
## 10 HUÁNUCO 100000 15.6 185658 253200 447620
## 11 ICA 110000 28.3 276632 369753 831180
## 12 JUNÍN 120000 11.6 377618 512532 993029
## 13 LA LIBERTAD 130000 21.8 489909 691563 1516546
## 14 LAMBAYEQUE 140000 19.4 345668 459254 1006609
## 15 LIMA 150000 31.4 4887993 4536507 10012201
## 16 LORETO 160000 15.1 218179 300663 695083
## 17 MADRE DE DIOS 170000 17.3 57440 64206 137927
## 18 MOQUEGUA 180000 19.0 93002 82399 167704
## 19 PASCO 190000 7.8 72646 97392 184423
## 20 PIURA 200000 22.0 480596 665465 1535498
## 21 PUNO 210000 7.7 307698 454941 714334
## 22 SAN MARTÍN 220000 16.2 198605 321613 613683
## 23 TACNA 230000 40.5 163436 161903 329169
## 24 TUMBES 240000 17.3 85685 90571 219639
## 25 UCAYALI 250000 14.9 161228 189783 444790
## PobRural pobTotal
## 1 211389 417365
## 2 333050 1139115
## 3 180905 424259
## 4 76739 1460433
## 5 206467 650940
## 6 860386 1427527
## 7 0 1046953
## 8 449449 1315220
## 9 201089 367252
## 10 312342 759962
## 11 62112 893292
## 12 323864 1316894
## 13 372426 1888972
## 14 238212 1244821
## 15 122808 10135009
## 16 286814 981897
## 17 23277 161204
## 18 14313 182017
## 19 87713 272136
## 20 394472 1929970
## 21 512602 1226936
## 22 248776 862459
## 23 19887 349056
## 24 15059 234698
## 25 104208 548998str(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 ...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)
str(data$contribuyentesSunat)## num [1:25] 75035 302906 103981 585628 151191 ...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: 0.1546library(modelsummary)## Warning: package 'modelsummary' was built under R version 4.4.1## `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)modelo1_st = formula(scale(buenEstado)~scale(contribuyentesSunat)+scale(peaOcupada))
modelo1_st = lm(modelo1_st, data = data)
summary(modelo1_st)##
## Call:
## lm(formula = modelo1_st, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2821 -0.4802 -0.1631 0.2309 2.6052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.313e-16 1.919e-01 0.000 1.000
## scale(contribuyentesSunat) 2.034e+00 2.346e+00 0.867 0.395
## scale(peaOcupada) -1.670e+00 2.346e+00 -0.712 0.484
##
## Residual standard error: 0.9595 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: 0.1546Al 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):
modelo2=formula(peaOcupada~contribuyentesSunat+buenEstado)
reg2=lm(modelo2,data=data)
summary(reg2)##
## 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-16library(modelsummary)
modelo2_st = formula(scale(peaOcupada)~scale(contribuyentesSunat)+scale(buenEstado))
modelo2_st = lm(modelo2_st, data = data)
summary(modelo2_st)##
## Call:
## lm(formula = modelo2_st, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10626 -0.06775 -0.01292 0.05341 0.18027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.043e-17 1.724e-02 0.000 1.000
## scale(contribuyentesSunat) 1.001e+00 1.894e-02 52.872 <2e-16 ***
## scale(buenEstado) -1.349e-02 1.894e-02 -0.712 0.484
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08622 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