In the model ln(y) = $\beta$₀ + $\beta$₁x + $\varepsilon$, the predicted value is = exp (b₀ + b₁x + ÷ 2). What is the impact of the estimated slope coefficient? A) b1measures the approximate change in when x increases by 1 unit. B) b₁× 0.01 measures the approximate change in when x increases by 1%. C) b1measures the approximate change in when x increases by 1%. D) b₁× 100 measures the approximate change in when x increases by 1 unit.

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The Answer of In the model ln(y) = $\beta$₀ + $\beta$₁x + $\varepsilon$, the predicted value is = exp (b₀ + b₁x + ÷ 2). What is the impact of the estimated slope coefficient? A) b1measures the approximate change in when x increases by 1 unit. B) b₁× 0.01 measures the approximate change in when x increases by 1%. C) b1measures the approximate change in when x increases by 1%. D) b₁× 100 measures the approximate change in when x increases by 1 unit.

In the Model Ln(y) = β\betaβ 0 β\betaβ 1x ε\varepsilonε , the Predicted Value Is = Exp (B0

In the Model Ln(y) = $\beta$ ₀ $\beta$ ₁x $\varepsilon$ , the Predicted Value Is = Exp (B₀