Models

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fit_bern |> add_epred_draws(newdata = ndata) |> select(.epred) |> ggplot(aes(x = .epred)) + geom_density(aes(y = after_stat(count/sum(count)))) + scale_x_continuous(labels = scales::percent_format()) + scale_y_continuous(labels = scales::percent_format()) + labs(title = “Posterior Distribution for p”, subtitle = “There is a 95% chance for a value between XX and XX”, x = “Proportion, p, of Red Beads in Urn”, y = “Probability”) ```

$$ biden_i \sim Bernoulli(\rho) $$

This data is collected from you gov, and a poll and we seek to figure out the proportion of people that voted for Biden One doubt is the way you.gov surveyed this data, and if it might be skewed towards a certain type of people. The modelling approach is the bernoulli formula and the model is displaying this. The quantity of interest is the percentage of people that will vote for Biden

Tidy data frames (one observation per row) are particularly convenient for use in a variety of R data manipulation and visualization packages (Wickham 2014). However, when using Bayesian modeling functions like JAGS or Stan in R, we often have to translate this data into a form the model understands, and then after running the model, translate the resulting sample (or predictions) into a more tidy format for use with other R functions. tidybayes aims to simplify these two common (often tedious) operations. It also provides a variety of ggplot geometries aimed at making the visualization of model output easier.