only one question Stata output interpretation

4. This question is about understanding and interpreting Stata output. P – observed land price per acre WL – proportion of acreage that is wooded DA – distance from parcel to Dulles airport D75 – distance from parcel to I-75 A – acreage of parcel MO – months it takes for the parcel to be sold Given the information in the table below, answer the following questions: (1) Interpret the estimate of the coefficient on the acreage of parcel (Table 1). (2) Test whether the months it takes for the parcel to be sold has any effect on the observed land price per acre at the 5% significance level (Table 1). List the steps. (3) Test the hypothesis that NEITHER the proportion of acreage that is wooded NOR the distance from parcel to Dulles airport has any effect on the observed land price per acre at the 1% significance level. List the steps. (4) Based on the measure of AIC, what is the best model specification (MODEL 1 or MODEL 2)? And why? (5) Suppose that MODEL 2 is the true model. However, Simon forgot to add the variable LOG(A) in the regression. What are the consequences of the omission of the variable LOG(A)? (6) Simon forgot to report the adjusted R-squared. Can you help him to get them in both tables? (7) Suppose that MODEL 2 is the true model. However, Simon forgot to add the variable WL in the regression. Is there any problem for doing this? Briefly explain. 2 MODEL 1/Table 1 . reg logp wl da d75 loga mo Source | SS df MS Number of obs = ————-+—————————— F( 5, 39 33) = 10.03 Model | 6.30160081 5 1.26032016 Prob > F = 0.0000 Residual | 4.14460685 33 .125594147 R-squared = 0.6032 ————-+—————————–Total | 10.4462077 38 Adj R-squared = .274900202 Root MSE = .35439 —————————————————————————–logp | Coef. Std. Err. t P>|t| [95% Conf. Interval] ————-+—————————————————————wl | .1492065 .1323043 da | -.0069925 .0275574 d75 | -.0661488 .0331367 loga | -.2769022 .0479683 mo | .0202226 .0048238 _cons | 9.143132 .3621494 25.25 0.000 8.406333 9.87993 —————————————————————————— . estat ic —————————————————————————-Model | Obs ll(null) ll(model) df AIC BIC ————-+————————————————————–. | 39 -29.65081 -11.62441 6 35.24881 45.23018 —————————————————————————-Note: N=Obs used in calculating BIC; see [R] BIC note 3 MODEL 2/Table 2 . reg logp d75 loga mo Source | SS df MS Number of obs = ————-+—————————— F( 3, 39 35) = 16.41 Model | 6.10567499 3 2.035225 Prob > F = 0.0000 Residual | 4.34053266 35 .124015219 R-squared = 0.5845 ————-+—————————–Total | 10.4462077 38 Adj R-squared = .274900202 Root MSE = .35216 —————————————————————————–logp | Coef. Std. Err. t P>|t| [95% Conf. Interval] ————-+—————————————————————d75 | -.0670195 .0180798 -3.71 0.001 -.1037234 -.0303156 loga | -.2769179 .0462432 -5.99 0.000 -.3707967 -.1830392 mo | .0208382 .004768 4.37 0.000 .0111586 .0305178 _cons | 9.079233 .2043665 44.43 0.000 8.664347 9.49412 —————————————————————————— . estat ic —————————————————————————-Model | Obs ll(null) ll(model) df AIC BIC ————-+————————————————————–. | 39 -29.65081 -12.52509 4 33.05019 39.70443 —————————————————————————-Note: N=Obs used in calculating BIC; see [R] BIC note . 4