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The error variance can be Illustrated as σ^2 = E(μ^2) = Var(μ) = (1/n) * ∑(y - (b0 + b1x))^2

The simple linear regression model is of the form:

y = b₀ + b₁x + μ

y = the dependent variable

x = the independent variable

b₀ and b₁ = the parameters of the model, and μ is the error term.

The error term represents the difference between the observed value of the dependent variable and the value predicted by the model. In other words, it is the deviation of the observed y from the expected value of y, given the value of x.

The error variance, denoted by σ^2, is a measure of the spread of the error term. It is calculated as the average squared deviation of the error term from its mean value, which is zero:

σ^2 = E(μ^2) = Var(μ) = (1/n) * ∑(y - (b₀ + b₁x))^2

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Complete question

Given the simple linear regression model y=b0+b1xi+µ, derive the error variance.

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