A large company plans to develop a private subdivision. There are 6 house models to choose from and for each model, a client may opt to have a garage or a carport. Use A, B, C, D, E, and F as labels for the house models. a. Design a simulation to generate frequencies that can be used to approximate the probability of a client who will choose model B with a car garage.
To design the simulation, we can start by randomly selecting a house model from the options A, B, C, D, E, and F. Then, we randomly decide whether the client will choose a garage or a carport.
Here is a Python script demonstrating this simulation:
```python
import random
# Initialize variables to keep track of the number of times model B with a car garage is chosen
total_simulations = 10000
model_B_garage_count = 0
# Run the simulation
for _ in range(total_simulations):
chosen_model = random.choice(['A', 'B', 'C', 'D', 'E', 'F'])
garage_choice = random.choice(['Garage', 'Carport'])
if chosen_model == 'B' and garage_choice == 'Garage':
model_B_garage_count += 1
# Calculate the approximate probability
probability_model_B_garage = model_B_garage_count / total_simulations
print("Approximate probability of choosing model B with a garage:", probability_model_B_garage)
```
By running this simulation multiple times (e.g., 10,000 simulations), we can obtain a frequency that can be used to approximate the probability of a client choosing model B with a garage in the private subdivision.