Regression [R2] Model Attributes Question
A regression was performed to predict selling price of houses based on Price in dollars, Area in square feet, Lotsize in square feet, and Age in years. The R2 is 92%. The equation from this regression is given here
Price = 169328 + 35.3 Area + 0.718 Lotsize – 6543 Age
Which of the following statements is correct according to the model?
A. Each year a house ages, it is worth $6543 less.
B. Every extra square foot of area is associated with an additional $35.3 in average price, for houses with a given lot size and age.
C. Every additional dollar in price means lot size increases 0.718 square feet, for houses with a given area and age.
D. This model fits 92% of the data points.
E. None of the above are true.
I feel like it would be D since r-squared hence : R2 is a measure of how well the datapoints fit .
What do yall think?