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.
This exercise is part of the course
Statistical Simulation in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
sims, outcomes, p_rain, p_pass = 1000, [], 0.40, {'sun':0.9, 'rain':0.3}
def test_outcome(p_rain):
# Simulate whether it will rain or not
weather = np.random.choice(['rain', 'sun'], p=[____])
# Simulate and return whether you will pass or fail
test_result = np.random.choice(['pass', 'fail'], p=[____])
return test_result