```{r}
library(rmarkdown)
health <- read.table("health.txt", header = TRUE, sep = " ", dec = ".")
paged_table(health)
```

```{r}
Health <- health[2:5]

library(dplyr)
library(corrplot)
correlation_matrix<-cor(Health)
corrplot(correlation_matrix, order = 'hclust',addrect = 3)
```

```{r}
model <- lm(y ~ ., data = Health)

coefficients(model)
summary(model)
```

```{r}
library(ggfortify)
library(car)
autoplot(model,1:3)
```

The points are not well distributed around 0 -> [P1] is not verified
The points are not well distributed around 1 -> [P2] is not verified
The QQPlot is align with the line y = x, so it is globally gaussian -> [P4] is verified

```{r}
set.seed(0)
durbinWatsonTest(model)
```

The p-value is 0.58 > 0.05 -> We do not reject H0 so the residuals are not autocorrelated -> [P3] is verified

```{r}
library(GGally)
ggpairs(Health, progress = F)
```

We observe that the variable age is correlated with the variable y. There is a quadratic relation between both variables.

```{r}
Health2 <- Health
Health2$age_sq <- Health2$age^2
Health2 <- Health2[1:24,]

model2 <- lm(y ~ ., data = Health2)

summary(model2)
coefficients(model2)
```

```{r}
library(ggfortify)
library(car)
autoplot(model2,1:4)
```

The points are well distributed around 0 -> [P1] is verified
The points are not well distributed around 1 -> [P2] is verified
The QQPlot is align with the line y = x, so it is gaussian -> [P4] is verified

```{r}
set.seed(0)
durbinWatsonTest(model2)
```

The p-value is 0.294 > 0.05 -> We do not reject H0 so the residuals are not autocorrelated -> [P3] is verified

