Exercise

# Coin flips with prop_model

The function `prop_model`

has been loaded into your workspace. It implements a Bayesian model that assumes that:

- The
`data`

is a vector of successes and failures represented by`1`

s and`0`

s. - There is an unknown underlying proportion of success.
- Prior to being updated with data any underlying proportion of success is equally likely.

Assume you just flipped a coin four times and the result was *heads*, *tails*, *tails*, *heads*. If you code *heads* as a success and *tails* as a failure then the following R codes runs `prop_model`

with this data

```
data <- c(1, 0, 0, 1)
prop_model(data)
```

Instructions 1/2

**undefined XP**

The output of prop_model is a plot showing what the model learns about the underlying proportion of success from each data point in the order you entered them. At n=0 there is no data, and all the model knows is that it's equally probable that the proportion of success is anything from 0% to 100%. At n=4 all data has been added, and the model knows a little bit more.

- Take
`prop_model`

for a spin by entering the R-code above into the script window to the right.