#Examen PyE Unidad 4
#Elba Maria Ybarra
#2.Grafico Pairs
library(readxl)
rides <- read_excel("rides.xlsx")
View(rides)
names(rides)
## [1] "Rides" "Price" "Population"
pairs(rides)
cor(rides)
## Rides Price Population
## Rides 1.0000000 -0.9659529 0.8975653
## Price -0.9659529 1.0000000 -0.9148968
## Population 0.8975653 -0.9148968 1.0000000
#3.Regresion Lineal
raite <- lm(Rides ~ Population, data = rides)
summary(raite)
##
## Call:
## lm(formula = Rides ~ Population, data = rides)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31609.6 -2735.3 84.5 5610.8 14243.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.137e+05 4.658e+04 -6.736 4.65e-07 ***
## Population 2.820e-01 2.770e-02 10.179 2.24e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9577 on 25 degrees of freedom
## Multiple R-squared: 0.8056, Adjusted R-squared: 0.7978
## F-statistic: 103.6 on 1 and 25 DF, p-value: 2.238e-10
plot(rides$Population, rides$Rides, xlab = "Population", ylab = "Rides")
abline(raite)
#4.Predicciones
viaje <- data.frame(Population = seq(115,152))
predict(raite, viaje)
## 1 2 3 4 5 6 7
## -313699.6 -313699.3 -313699.1 -313698.8 -313698.5 -313698.2 -313697.9
## 8 9 10 11 12 13 14
## -313697.7 -313697.4 -313697.1 -313696.8 -313696.5 -313696.2 -313696.0
## 15 16 17 18 19 20 21
## -313695.7 -313695.4 -313695.1 -313694.8 -313694.6 -313694.3 -313694.0
## 22 23 24 25 26 27 28
## -313693.7 -313693.4 -313693.1 -313692.9 -313692.6 -313692.3 -313692.0
## 29 30 31 32 33 34 35
## -313691.7 -313691.5 -313691.2 -313690.9 -313690.6 -313690.3 -313690.0
## 36 37 38
## -313689.8 -313689.5 -313689.2
#5.Intervalos de Confianza
confint(raite)
## 2.5 % 97.5 %
## (Intercept) -4.096617e+05 -2.178024e+05
## Population 2.249278e-01 3.390327e-01
ic <- predict(raite, viaje, interval = "confidence")
lines(viaje$Population, ic[,2], lty=2)
lines(viaje$Population, ic[,3], lty=2)
ic <- predict(raite, viaje, interval = "prediction")
lines(viaje$Population, ic[,2], lty=2, col = "red")
lines(viaje$Population, ic[,3], lty=2, col = "red")
#6. ANOVA
anova(raite)
## Analysis of Variance Table
##
## Response: Rides
## Df Sum Sq Mean Sq F value Pr(>F)
## Population 1 9504065441 9504065441 103.62 2.238e-10 ***
## Residuals 25 2293089963 91723599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#7. Grafico de Residuos
residuos <- rstandard(raite)
valores.ajustados <- fitted(raite)
plot(valores.ajustados, residuos)
#8. Grafico Quantil-Quantil
qqnorm(residuos)
qqline(residuos)
