Generate data and fit the linear model

# Generate  data
set.seed(123)
x <- 1:100
y <- 2*x + rnorm(100, mean = 0, sd = 10)

# Fit linear regression model
model <- lm(y ~ x)
summary(model)
## 
## Call:
## lm(formula = y ~ x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -24.5356  -5.5236  -0.3462   6.4850  20.9487 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.36404    1.84287  -0.198    0.844    
## x            2.02511    0.03168  63.920   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.145 on 98 degrees of freedom
## Multiple R-squared:  0.9766, Adjusted R-squared:  0.9763 
## F-statistic:  4086 on 1 and 98 DF,  p-value: < 2.2e-16

Residual analysis

# Plot a Q-Q plot of residuals
qqnorm(model$residuals)
qqline(model$residuals)

The points in the QQ plot approximately follow a straight line, indicating that the residuals are normally distributed.