Question 1. The table below gives the OLS regression results for a Cobb Douglas production function. The dataset is for the manufacturing sector in the USA, covering 50 states and Washington, D.C. for the year 2005. Inoutput,=Bi+ B2Incapital;+ Balnlabor; where Inoutput stands for the natural logarithm of output (in thousand of dollars) for state i. Incapital stands for the natural logarithm of capital expenditure (in thousand dollars). Inlabor stands for the natural logarithm of labor (in thousand of workers' hours). Bi stands for a constant (_cons). reg Inoutput Incapital Inlabor Source SS df MS Number of obs $1 F (2, 48) 645.93 Model 91. 9246133 2 45. 9623067 Prob > F 0. 0000 Residual 3. 41551772 . 071156619 R-squared 5. 9642 Adj R-squared 0. 9627 95.340131 50 1.90680262 11 Total Root MSE -26675 Inoutput Coef. Std. Err. t P>ItI [954 Conf. Interval] Incapital 5212795 . 096887 5.38 0.000 326475 7160839 Inlabor 4683318 0989259 4.73 0.000 .269428 6672357 _cons 3. 887599 . 3962281 9.81 D.000 3.090929 4 . 684269 1.a) Which of the explanatory variables (also known as regressors) are statistically significant at the 10% significance level? [5 marks] 1.b) Explain how you would interpret the regression coefficient of Inlabor. [5 marks] 1.c) Explain how you would interpret the R-squared statistic of this regression? [5 marks] 1.d) How would you test whether the OLS model presented suffers from multicollinearity given that labour and capital could be correlated? Be explicit as to what values this test would need to take for you to conclude that the regression suffers from multicollinearity. [5 marks]