Sure, here is the Julia code to simulate example data for a general linear model:

```julia
using StatsBase

function simulate_data(n_samples, n_features, beta, sigma)
  """Simulates example data for a general linear model.

  Args:
    n_samples: The number of samples to simulate.
    n_features: The number of features.
    beta: The coefficient vector.
    sigma: The noise standard deviation.

  Returns:
    A tuple of (X, y), where X is the design matrix and y is the response vector.
  """

  X = randn(n_samples, n_features)
  y = X * beta + sigma * randn(n_samples)
  return X, y
end

if __name__ == "__main__":
  n_samples = 100
  n_features = 5
  beta = randn(n_features)
  sigma = 0.1
  X, y = simulate_data(n_samples, n_features, beta, sigma)
  print(X.shape)
  print(y.shape)
```

This code first defines a function called `simulate_data()` that takes in the number of samples, the number of features, the coefficient vector, and the noise standard deviation, and returns a tuple of the design matrix and the response vector. The design matrix is a matrix of features, and the response vector is a vector of observations.

The function then simulates the data by first generating a random matrix of features using the `randn()` function. The `randn()` function generates a matrix of random numbers from the standard normal distribution. The function then adds a noise term to the matrix of features, using the `sigma` parameter to control the standard deviation of the noise.

Finally, the function returns the simulated design matrix and response vector.

The main function of the code then simulates the data with the given parameters, and prints the shapes of the design matrix and the response vector.

To run the code, you can save it as a `.jl` file and then run it from the command line using the `julia` command. For example, if you save the code as `simulate_data.jl`, you can run it by typing the following command into the command line:

```
julia simulate_data.jl
```

This will run the code and print the shapes of the design matrix and the response vector.
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