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.
Diese Übung ist Teil des Kurses
Statistical Simulation in Python
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
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