DataCamp

Simulating the dependence of X on p

In your quest for election to public office, your campaign polls 10 likely voters. Let X be the number that support you. Of course, X varies from sample to sample and depends upon p, your underlying support in the broader population. Since X is a count of successes in 10 independent trials, each having probability of success p, you can model its dependence on p by the Binomial distribution: Bin(10,p).

You will simulate the Binomial model using random samples from the rbinom(n, size, prob) function. This vectorized function draws n samples from a Bin(size, prob) distribution. Given a vector of prob values, the first prob value will be used for the first draw, the second prob value will be used for the second draw, etc.

Define a seq() of 1000 possible values of p that range from 0 to 1. Store this as p_grid.
Use rbinom() to simulate one poll result X for each of the 1000 p in p_grid. Assign these to poll_result.
The likelihood_sim data frame combines p_grid and poll_result. Use ggplot() with a geom_density_ridges() layer to illustrate the distribution of p_grid values (x axis) from which each poll_result was simulated (y axis).

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Exercise

Simulating the dependence of X on p

Instructions