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. 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.

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3 answers

To simulate the probability of a client choosing model B with a car garage, we can create a simple simulation using random number generation. Here's how we can do it:

1. Assign values to each of the 6 house models (A, B, C, D, E, F) where:
- Models A, B, and C have a garage
- Models D, E, and F have a carport

2. Generate random numbers from 1 to 6 to represent the client's choice of house model.

3. Generate another random number from 1 to 2 to represent the client's choice of garage (1 for garage, 2 for carport).

4. Repeat the simulation multiple times to get a good approximation of the probability.

Here's a Python code snippet to perform this simulation:

```python
import random

# Initialize variables
num_simulations = 100000
num_b_with_garage = 0

# Run simulation
for _ in range(num_simulations):
house_model = random.randint(1, 6)
garage_choice = random.randint(1, 2)

if house_model == 2 and garage_choice == 1: # Check if model B with a garage is chosen
num_b_with_garage += 1

# Calculate probability
probability = num_b_with_garage / num_simulations

print("Approximate probability of a client choosing model B with a car garage:", probability)
```

When you run this simulation, you will get an approximate probability of a client choosing model B with a car garage based on the given house models and garage options.

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