Kindly answer all parts.

Model 1: OLS, using observations 1-112 Dependent variable: y coefficient std. error t-ratio p-value const 1 . 89126 2 . 62056 0 . 7217 0 . 4720 x1 1 .29887 0 . 525983 2. 469 0 . 0151 x2 2 . 08290 1 . 50896 1 . 380 0. 1703 x3 -1 . 76258 0 . 496742 -3.548 0 . 0006 Mean dependent var 1 . 090835 S. D. dependent var 4. 963217 Sum squared resid 2312. 352 S.E. of regression 4 . 627166 R-squared 0 . 154323 Adjusted R-squared 0 . 130832 F (3, 108) 6.569454 P-value (F) 0 . 000403 Log-likelihood -328 . 4623 Akaike criterion 664. 9247 Schwarz criterion 675. 7987 Hannan-Quinn 669. 3366 Excluding the constant, p-value was highest for variable 2 (x2)Consider the following regression models: A. yi = Bo + Bixli + wi B. yi = Bo + Bili + B2x2i + Baxsitei C. yi = do + alln(x1i) + azln(x2i) + agln(x3i) + Ei Data for the variables is in this file. Answer the following questions about these models.Using a MizonRichard test, test the null hypothesis that Model B is the correct specification against the alternative hypothesis that Model C is the correct specification. Use the pvalue method. Enter the value of the test statistic in the box below (accurate to 3 decimal places) Enter the pvalue in the box below (accurate to 3 decimal places) Using a significance level of 10%, do you find evidence against Model B