Sure! Here's an example of Julia code to simulate example data for a general linear model (GLM):

```julia
using Random

# Set the random seed for reproducibility
Random.seed!(123)

# Number of samples
n = 100

# Number of predictors (including the intercept)
p = 3

# Generate predictor variables (including the intercept)
X = [ones(n) randn(n, p-1)]

# True coefficients for the model
true_coefficients = [2.0, 1.5, -0.5]

# Generate the response variable based on the true coefficients and predictor variables
y = X * true_coefficients .+ randn(n)

# Display the first few rows of the simulated data
println("Simulated Data:")
println("X = ")
println(X[1:5, :])
println("y = ")
println(y[1:5])
```

In this example, we generate random data for a GLM with three predictor variables (including the intercept). The true coefficients of the model are [2.0, 1.5, -0.5]. We then generate the response variable `y` based on the true coefficients and predictor variables `X`. The `randn` function is used to introduce random noise to the response variable. The result is a dataset (`X` and `y`) that can be used to fit a general linear model in Julia.
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