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Exercise

Driving test

Through the next exercises, we will learn how to build a data generating process (DGP) through progressively complex examples.

In this exercise, you will simulate a very simple DGP. Suppose that you are about to take a driving test tomorrow. Based on your own practice and based on data you have gathered, you know that the probability of you passing the test is 90% when it's sunny and only 30% when it's raining. Your local weather station forecasts that there's a 40% chance of rain tomorrow. Based on this information, you want to know what is the probability of you passing the driving test tomorrow.

This is a simple problem and can be solved analytically. Here, you will learn how to model a simple DGP and see how it can be used for simulation.

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  • Write a function test_outcome().
    • Set weather as 'rain' or 'sun' depending on the input argument p_rain (the probability of rain).
    • Set the appropriate probabilities of 'pass' and 'fail' in test_result using weather & the dictionary p_pass.
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