my_awesome_line.R

a <- 2
b <- 3
sig_sq <- 1

x <- runif(40)
y <- a + b * x + rnorm(40, sd = sqrt(sig_sq))

(avg_x <- mean(x))

## [1] 0.4657908

write(avg_x, "avg_x.txt")

plot(x, y)
abline(a = a, b = b, col = "hotpink", lwd = 2)

my_fit <- lm(y ~ x)
summary(my_fit)

## 
## Call:
## lm(formula = y ~ x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.42438 -0.67534  0.06124  0.72514  2.53697 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2.0440     0.3293   6.207 2.96e-07 ***
## x             2.9385     0.5979   4.914 1.73e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.111 on 38 degrees of freedom
## Multiple R-squared:  0.3886, Adjusted R-squared:  0.3725 
## F-statistic: 24.15 on 1 and 38 DF,  p-value: 1.734e-05