We estimate the following model where price je is the price of house j in year t and is a Exercise 1. function of the distance to the nearest recreation park using a dataset with 578 observations. In particular, the variable in the model is measured as the difference between the distance of each house to the park and 150 yards. The OLS regression line is priče = 850000 + 164 (distance - 150) (150000) (41) Where standard errors of the estimated coefficients are in parentheses.
We estimate the following model where price je is the price of house j in year t and is a Exercise 1. function of the distance to the nearest recreation park using a dataset with 578 observations. In particular, the variable in the model is measured as the difference between the distance of each house to the park and 150 yards. The OLS regression line is priče = 850000 + 164 (distance - 150) (150000) (41) Where standard errors of the estimated coefficients are in parentheses.
Chapter4: Linear Functions
Section4.3: Fitting Linear Models To Data
Problem 34SE: For the following exercises, consider this scenario: The profit of a company decreased steadily...
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Transcribed Image Text:We estimate the following model where price je is the price of house j in year t and is a
Exercise 1.
function of the distance to the nearest recreation park using a dataset with 578 observations. In particular, the
variable in the model is measured as the difference between the distance of each house to the park and 150 yards.
The OLS regression line is
price j = 850000 + 164 (distancej, – 150)
(150000) (41)
Where standard errors of the estimated coefficients are in parentheses.
Transcribed Image Text:When we predict prices and perform the correlation of actual and predicted prices we obtain a
value of 0.48. What is the regression's goodness of fit measure adjusted R-squared's value? Round all
calculations and final answer to 4 decimal places.
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