1. Consider the linear regression models. The p-values are shown in parentheses [5 points). Model 1 Y=123.2 +0.89X₁ Model 2 Model 3 (0.0005) (0.00001) Y = 40.1 +0.14X₂ +0.61X, (0.003) (0.008) (0.0004) Y=-1.7+2.57X₁-1.78X₂ (0.006) (0.04) (0.2) R² = 0.600 R² = 0.825 R² = 0.876 Where Y is the number of trips, X, is the total employment, X₂ is the industrial employment, X, is the commercial employment. Choose the most appropriate model and explain why this model is selected as the most appropriate model. Hint: Consider both goodness-of-fit and coefficient significance.

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1. Consider the linear regression models. The p-values are shown in parentheses [5 points].
Model 1
Y = 123.2 +0.89X₂
R² = 0.600
Model 2
Model 3
(0.0005) (0.00001)
Y = 40.1 +0.14X₂ + 0.61X3
(0.003) (0.008) (0.0004)
Y=-1.7+2.57X₁-1.78X₂
(0.006) (0.04) (0.2)
R² = 0.825
R² = 0.876
Where Y is the number of trips, X₁ is the total employment, X₂ is the industrial employment, Xs is the commercial
employment. Choose the most appropriate model and explain why this model is selected as the most appropriate model.
Hint: Consider both goodness-of-fit and coefficient significance.
Transcribed Image Text:1. Consider the linear regression models. The p-values are shown in parentheses [5 points]. Model 1 Y = 123.2 +0.89X₂ R² = 0.600 Model 2 Model 3 (0.0005) (0.00001) Y = 40.1 +0.14X₂ + 0.61X3 (0.003) (0.008) (0.0004) Y=-1.7+2.57X₁-1.78X₂ (0.006) (0.04) (0.2) R² = 0.825 R² = 0.876 Where Y is the number of trips, X₁ is the total employment, X₂ is the industrial employment, Xs is the commercial employment. Choose the most appropriate model and explain why this model is selected as the most appropriate model. Hint: Consider both goodness-of-fit and coefficient significance.
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