Figure 1 explores the impact of Value on Wage.
library(ggplot2)
library(dplyr)
players_orig <- read.csv("./../Datasets/FIFA22/players_22.csv")
players <- players_orig %>% sample_frac(0.01)
ggplot(data=players,
mapping = aes(x=value_eur, y=wage_eur))+
geom_point(color='cornflowerblue',
alpha = 0.7,
size=3)+
geom_smooth(method = 'lm')
The following figures show the fit of the model used to predict Wage.
M0 <- lm(wage_eur ~ value_eur+age+height_cm+weight_kg, data=players)
summary(M0)
Call:
lm(formula = wage_eur ~ value_eur + age + height_cm + weight_kg,
data = players)
Residuals:
Min 1Q Median 3Q Max
-97769 -4611 -1773 583 80193
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.206e+04 2.889e+04 -0.763 0.44623
value_eur 1.909e-03 1.138e-04 16.777 < 2e-16 ***
age 6.097e+02 2.238e+02 2.724 0.00707 **
height_cm 1.870e+02 2.091e+02 0.894 0.37237
weight_kg -3.106e+02 2.125e+02 -1.462 0.14556
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13870 on 185 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.6142, Adjusted R-squared: 0.6059
F-statistic: 73.64 on 4 and 185 DF, p-value: < 2.2e-16plot(M0)
