To fit a linear model to the simulated data in Julia, you can use the LinearModels package, which provides tools for linear regression. Here's how you can do it:

First, make sure you have the LinearModels package installed. You can install it by running the following command in the Julia REPL (the Julia interactive environment):

```julia
using Pkg
Pkg.add("LinearModels")
```

Now, you can use the simulated data to fit a linear model:

```julia
using LinearModels

# Simulated data (already generated in the previous code)
n_samples = 100
n_features = 5
beta = randn(n_features)
sigma = 0.1
X, y = simulate_data(n_samples, n_features, beta, sigma)

# Fit the linear model
model = lm(y, X)

# Print the model summary
@show(model)
```

In this code, we first load the LinearModels package. Then, we use the simulated data `X` (design matrix) and `y` (response vector) that we generated in the previous code.

The `lm()` function is used to fit the linear model. It takes the response vector `y` and the design matrix `X` as input and returns a `LinearModel` object.

Finally, we print the summary of the fitted model using `@show(model)`. This will display the estimated coefficients (beta values) for each feature, along with other statistical information about the model fit.

By following these steps, you can fit a linear model to the simulated data and analyze the results.
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