Question

The following show the results of regression:

Housing Sold = b0 + b1 permit +b2 price + b3 employment

Dependent Variable: SOLD ,             
Method: Least Squares              
Date: 03/15/20   Time: 14:59              
            
Included observations: 108              
              
Variable      Coefficient Std. Error t-Statistic    Prob.
              
C -61520.76   167763.0            -0.366712      0.7146
PERMIT    15.98282                .280962   12.47721     0.0000
PRICE      -0.360566   0.345253      -1.044354 0.2987
EMP 33.57386    15.67645   2.141674 0.0346
              
R-squared   0.634646         Mean dependent var       481456.3
Adjusted R-squared   0.624106        S.D. dependent var       62292.29
S.E. of regression   38191.50          Akaike info criterion      23.97495
Sum squared resid   1.52E+11      Schwarz criterion          24.07429
Log likelihood   -1290.647       Hannan-Quinn criter.       24.01523
F-statistic   60.21835                        Durbin-Watson stat      0.553692
Prob(F-statistic)   0.000000          

Choose the correct answer

Multiple Choice

• The R squared of this regression is not relaible.

• The estimated coefficients are biased.

• The esstimated coefficients are unbiased but inefficient.

• options a and c are correct.

Answer 1

From the output of regression model the regression equation is

Y=-61520.76+15.9828 permit -0.3606price +33.5739 employment.

And the value of R square=.6347

We can say that 63.47%variation in dependent variable can be explained by independent varible which is very less so it is not reliable also from the property of estimated coefficient\beta they are unbiased but for this model they are not efficient because they do not fit this model in accurate way eesulting large gap between observed and fitted values.

So option a and c are correct