library(readxl)
Provincia_ExamenParcial <- read_excel("C:/Users/Mariano/Desktop/Estadistica 2/Examen Parcial/Provincia ExamenParcial.xlsx")
## New names:
## • `Población de niños menores de un año
(CENSO 2017)
1a/` -> `Población de
## niños menores de un año
(CENSO 2017)
1a/...6`
## • `Población de niños menores de un año
(CENSO 2017)
1a/` -> `Población de
## niños menores de un año
(CENSO 2017)
1a/...18`
View(Provincia_ExamenParcial)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(rio)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.4
## ✔ ggplot2 3.4.3 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── 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 <- Provincia_ExamenParcial[c(1,2,25, 30, 43, 15)]
colnames(data) <- c("UBIGEO", "Provincia", "IVIA", "IDE", "Devengado_INV", "NumeroDistritos")
model <- lm(Devengado_INV ~ IVIA + IDE, data=data)
summary(model)
##
## Call:
## lm(formula = Devengado_INV ~ IVIA + IDE, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1381 -719 -361 83 11542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -278.6 1452.3 -0.192 0.848
## IVIA 1255.8 787.2 1.595 0.112
## IDE 1591.7 1697.0 0.938 0.349
##
## Residual standard error: 1437 on 193 degrees of freedom
## Multiple R-squared: 0.01304, Adjusted R-squared: 0.002814
## F-statistic: 1.275 on 2 and 193 DF, p-value: 0.2817
model1 <- lm(Devengado_INV ~ IVIA + IDE, data=data, offset =NumeroDistritos)
summary(model1)
##
## Call:
## lm(formula = Devengado_INV ~ IVIA + IDE, data = data, offset = NumeroDistritos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1380.8 -713.0 -359.4 80.3 11545.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -262.3 1452.6 -0.181 0.857
## IVIA 1250.6 787.3 1.588 0.114
## IDE 1557.9 1697.4 0.918 0.360
##
## Residual standard error: 1437 on 193 degrees of freedom
## Multiple R-squared: 0.01295, Adjusted R-squared: 0.002721
## F-statistic: 1.266 on 2 and 193 DF, p-value: 0.2843
summary(model1)$coef[,-1]
## Std. Error t value Pr(>|t|)
## (Intercept) 1452.5860 -0.1805602 0.8569023
## IVIA 787.3197 1.5884905 0.1138123
## IDE 1697.3645 0.9178141 0.3598617
plot(model1)
anova(model1)
## Analysis of Variance Table
##
## Response: Devengado_INV
## Df Sum Sq Mean Sq F value Pr(>F)
## IVIA 1 3489233 3489233 1.6896 0.1952
## IDE 1 1739596 1739596 0.8424 0.3599
## Residuals 193 398562353 2065090
library(readxl)
admision <- read_excel("C:/Users/Mariano/Desktop/Estadistica 2/Examen Parcial/admision.xlsx")
View(admision)
regresion_logistica <- glm(admitido ~ letras + ciencias + prestigio, data = admision, family = "binomial")
summary(regresion_logistica)
##
## Call:
## glm(formula = admitido ~ letras + ciencias + prestigio, family = "binomial",
## data = admision)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.249705 1.155732 -5.408 6.39e-08 ***
## letras 0.002294 0.001092 2.101 0.0356 *
## ciencias 0.007770 0.003275 2.373 0.0177 *
## prestigio 0.560031 0.127137 4.405 1.06e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 499.98 on 399 degrees of freedom
## Residual deviance: 459.44 on 396 degrees of freedom
## AIC: 467.44
##
## Number of Fisher Scoring iterations: 4
library(readxl)
dataCarcel <- read_excel("C:/Users/Mariano/Desktop/Estadistica 2/Examen Parcial/dataCarcel.xlsx")
View(dataCarcel)
modelo_poisson <- glm(vecesEnCarcel ~ edad + casado, data = dataCarcel, family = "poisson")
# Obtener un resumen del modelo
summary(modelo_poisson)
##
## Call:
## glm(formula = vecesEnCarcel ~ edad + casado, family = "poisson",
## data = dataCarcel)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.50705 0.12158 12.395 < 2e-16 ***
## edad -0.01686 0.00499 -3.379 0.000727 ***
## casado -0.03603 0.08935 -0.403 0.686761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1008.54 on 431 degrees of freedom
## Residual deviance: 995.44 on 429 degrees of freedom
## AIC: 2111.3
##
## Number of Fisher Scoring iterations: 5