Question 2, 3, and 4 please log(Quantity-b\" + bylog(Capital) + blog (Hours) + e

Question

Question 2, 3, and 4 please

log(Quantity-b" + bylog(Capital) + blog (Hours) + et the basic production model and report regression resuits. Include 1. Estimate coerrieients, standard errors, and t-statics. (Note: You vill have to create nev variables for estination) Generate inquantity-log (quantity) do this for capital and hours and then regress Inquantity Incapital Inhours Source 1 dr MS 248 160.75 -0.0000 0.5675 Adj R-squared 0.5640 1.1244 Number of Oba Model 1 406.50484 Residual 1 309.771084 P(2, 245) 2 203.25242 Prob >r 245 1.26437177 R-squared Total I 716.275924 247 2.89990253 Root MSE Inquantity I Coef Std. Err. tPlt [958 Conf. Intervall incapital 2554895 047833 Inhours 4294179 0594522 34 0.000 7.22 0.000 161273 3497059 3123154 5465205 cons 1 2:143711 1304278 16.44 0.000 1.886808 2.400614 2, Based on your zesalF11hat ft the narginal reure to hotara spent tishang, Be :.creful to neerpret ecetticiens onlog-transtormed variables correctly. Dces the eatinated production function have conecant recurns to scale decreasing recurns ta acsie, incressing retuzns

Explanation / Answer

2.

Marginal retuen to hours spent fishing is given by the coefficient of Inhours

in the last table. Now, since the Inquanitiy is essentially

a log figure we take out the anti log of Inhours coefficient as

Antilog( inhours) = antilog(.429) = 1.5357.

Which means that the marginal increase due to inhours in 1.5357 units.

3. It is increasing returns to scale as the coefficients are positive

4.

The hypothesis tests are already done in the last table. Check the p-value column of this table.

We see that the p-values of all coefficients are 0.00, which is less than .05 ( the significance cutoff )

Hence, we conclude that all the coefficients i.e. Inhours and Incapital are significant from the hypothesis test.

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