Regresion Lineal: Goles vs Titulos

knitr::opts_chunk$set(echo = TRUE)
library(readxl)
library(ggplot2)
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

# Leemos el Excel
datos <- read_excel("Equipos.xlsx", skip = 2)

# Calculamos correlación
correlacion <- cor(datos$Goles, datos$`Títulos`)
print(paste("Correlación:", correlacion))

## [1] "Correlación: -0.271522439435476"

# Creamos el modelo
modelo <- lm(`Títulos` ~ Goles, data = datos)
summary(modelo)

## 
## Call:
## lm(formula = Títulos ~ Goles, data = datos)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1225 -1.7209 -0.9092 -0.1171 11.3269 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  5.92009    2.35760   2.511   0.0199 *
## Goles       -0.08988    0.06792  -1.323   0.1993  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.124 on 22 degrees of freedom
## Multiple R-squared:  0.07372,    Adjusted R-squared:  0.03162 
## F-statistic: 1.751 on 1 and 22 DF,  p-value: 0.1993

ggplot(datos, aes(x = Goles, y = `Títulos`)) +
  geom_point(color = "blue", size = 3) +
  geom_smooth(method = "lm", color = "red") +
  labs(title = "Regresión Lineal Simple", x = "Goles", y = "Títulos") +
  theme_minimal()

## `geom_smooth()` using formula = 'y ~ x'

par(mfrow=c(2,2))
plot(modelo)