Using the regression below on the real estate dataset, examining the relationshi
ID: 3295452 • Letter: U
Question
Using the regression below on the real estate dataset, examining the relationship between residential sales price, measured in dollars (“sold_price”), and the number of days the property was listed on the market before it was sold (“days_on_market”) answer the followingCan you reject the null hypothesis? Write the equation of the line the results represent. Describe in words what the value of the regression coefficient means. df Number of ob = 3858 F( 1, 3856)= 326.40 Prob> F R-squared = 0.0780 Adj R-squared 0.0778 Root MSE Source I MS 1 7.1182e+13 Residual 8.4093e+14 3856 2.1808e+11 Mode 7.1182 +13 0.0000 Total 9.1211e+14 3857 2.3648e+11 -4.7e+05 sold price Coef. Std. Err t p>It' [95% Conf. Interval] days on t890.0965 49.26778 18.07 .000 793.5031 986.6899 83255.1 420519.3 cons 401887.2 9503.364 42.29 0.000
Explanation / Answer
Note that, the test-statistic for testing the significance of the model = 326.40.
p-value = P(F > 326.40) where, F ~ F-distribution with (1,3856) d.f.
Hence, P-value = 0. Since P-value < any given level of significance, we reject the null hypothesis and conclude that the model is significant.
The regression equation is,
Y = 401887.2 + 890.0965 X
where, Y: sold price (in dollars) and X: number of days in the market.
The co-efficient of intercept i.e. the constant term implies that when number of days in the market is 0, the sold price is 401887.2 dollars.
The co-efficient of slope i.e. number of days in the market implies increase by 1 day time period will increase the sold price by an amount of 890.0965 dollars.
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