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Simulate \(y\) data for a linear regression model with one continuous predictor \(x_1\) and one binary predicor \(x_2\). This has a varying intercept.
dg$y = 0 + 2*x1 + 3*x2 + rnorm(n, 0, .1) g = ggplot(dg, aes(x1, y, group = x2))+ geom_point(alpha = 0.1) g %>% pub(xlim = c(0,1))
[1] 20 [1] 80 [1] 20 [1] 120