synthetic_linear_model.R

## Synthetic data ----
set.seed(200)
n = 100
x <- 1:n
y <- x + rnorm(n = 50, mean = 15, sd = 15)

## Synthetic linear model ----
m0 <- lm(y ~ x)
summary(m0)
# Residuals:
#   Min       1Q   Median       3Q      Max
# -27.6093  -6.4396   0.6437   6.7742  26.7192
#
# Coefficients:
#   Estimate Std. Error t value Pr(>|t|)
# (Intercept) 14.84655    2.37953   6.239 1.12e-08 ***
#   x            0.97910    0.04091  23.934  < 2e-16 ***
#   ---
#   Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 11.81 on 98 degrees of freedom
# Multiple R-squared:  0.8539,	Adjusted R-squared:  0.8524
# F-statistic: 572.9 on 1 and 98 DF,  p-value: < 2.2e-16