DataCamp

Visualizing the regression priors

likelihood: \\(Y\\) i \\(\\sim N(m\\) i , \\(s^2)\\) where \\(m\\) i \\(= a + b X\\) i
priors: \\(a \\sim N(0, 200^2)\\), \\(b \\sim N(1, 0.5^2)\\) and \\(s \\sim Unif(0, 20)\\)

In the previous exercise, you simulated 10,000 samples for each parameter (\(a\), \(b\), \(s\)) in the Bayesian regression model of weight \(Y\) by height \(X\): \(Y \sim N(m, s^2)\) with mean \(m = a + bX\). The set of \(a\), \(b\), and \(s\) values in each row of samples represents a prior plausible regression scenario. To explore the scope of these prior scenarios, you will simulate 50 pairs of height and weight values from each of the first 12 sets of prior parameters \(a\), \(b\), and \(s\).

Create a data frame prior_simulation which includes n = 50 replicates of the first 12 sets of prior parameters in samples (600 rows in total!).
For each of the 600 prior_simulation rows:
- Simulate a height value from a \(N(170, 10^2)\) model.
- Simulate a weight value from \(N(a + b X, s^2)\) where \((X,a,b,s)\) are the corresponding set of height and prior parameters.
You now have 50 simulated height and weight pairs for each of the 12 parameter sets. Use ggplot() to construct a scatterplot of these 50 pairs for each set of parameter values. Be sure to put weight on the y-axis!

Take Hint (-30 XP)

Exercise

Visualizing the regression priors

Instructions