Consider the following regression estimates (FN2)Linear regressionbelavgabvavgfemalemarried_consb. 1wageO C. 5O d. 4RobustCoef. Std. Err.304784531502022820787-1.063254.0693348-2.751963.9686236 .26126466.699098 .2889831Number of obsF(4, 1255)Prob > FR-squaredRoot MSEt P>|t|-3.490.0010.22 0.826-9.76 0.0003.71 0.00023.18 0.000||||||||||==.456066.132155=-1.661197-.5486894-3.305361==[95% Conf. Interval]1,26056.350.00000.11214.3987assume that MLR 1-6 hold.In the regression above, how many coefficients (including the constant) are statistically significant at the 1% level?a. 3-.4653108.687359-2.1985651.4811877.266042

In order to check that coefficients are statistically significant at the 1% level, we need to check…

Consider the following regression estimates (FN2) Linear regression wage belavg -1.063254 .3047845 abvavg .0693348 .3150202 female -2.751963 .2820787 married .9686236 .2612646 _cons 6.699098 .2889831 0 C. 5 Robust Coef. Std. Err. O d. 4 t Number of obs F(4, 1255) Prob > F R-squared Root MSE P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 = = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] .45606 6.132155 1,260 56.35 0.0000 0.1121 4.3987 assume that MLR 1-6 hold. In the regression above, how many coefficients (including the constant) are statistically significant at the 1% level? a. 3 O b. 1 -.4653108 .687359 -2.198565 1.481187 7.266042

Consider the following regression estimates (FN2) Linear regression wage belavg -1.063254 .3047845 abvavg .0693348 .3150202 female -2.751963 .2820787 married .9686236 .2612646 _cons 6.699098 .2889831 0 C. 5 Robust Coef. Std. Err. O d. 4 t Number of obs F(4, 1255) Prob > F R-squared Root MSE P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 = = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] .45606 6.132155 1,260 56.35 0.0000 0.1121 4.3987 assume that MLR 1-6 hold. In the regression above, how many coefficients (including the constant) are statistically significant at the 1% level? a. 3 O b. 1 -.4653108 .687359 -2.198565 1.481187 7.266042

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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subparts a, b and c please
wages = B1 + B2educ + ßzexper + e where wages denotes hourly wages. We estimate the regression in R and obtain the output ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.0 1.0 2 0.0455 * ## educ 0.5 0.5 1 0.3173 ## exper 2.0 0.5 4 6.33e-05 *** ## --- ## Signif. codes: ****' 0. 001 '**' 0.01 **' 0.05 ' 0.1 ' ' 1 Build a 90% confidence interval for B3 using a normal approximation. (Use that if Z ~ N(0, 1) and z1-a satisfies P(Z > z1-a) = a, then zo9 = 1.28, zo95 = 1.64, Z0.975 = 1.96, zo.99 = 2.33, and z0.995 = 2.58). Oa. [2 – 1.64 x (0.5), 2 + 1.64 x (0.5)] O b. [2 – 1.28 × (0.5), 2 + 1.28 x (0.5)] c. [2 – 1.28 x (0.5)², 2 + 1.28 × (0.5)²] O d. [2 – 1.64 × (0.5)², 2 + 1.64 × (0.5)²] O e. [2 – 1.96 × (0.5)², 2 + 1.96 × (0.5)²] O f. [2 – 1.96 × (0.5), 2 + 1.96 × (0.5)]
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What is the ans for PURE specific and the ans for MIXED specific. Pls give explanations for the differences between these two models. (p/s this is the 3rd time I'm asking this question bcos your team did not answer my question of Pure AND Mixed but just specific factors model in general) PLEASE HELP ME THANKSSSS In the Pure Specific Factors model with two sectors, Cars (C) and Wheat (W), Capital (K) is specific to C and Land (A) is specific to W. If the government imposes a tariff on the imports of W then... In the Mixed Specific Factors model with two sectors, Cars (C) and Wheat (W), Capital (K) is specific to C and Land (A) is specific to W. If the government imposes a tariff on the imports of W then... A. Both owners of K and owners of A will benefit. B. Owners of A will benefit. C. Owners of K will benefit. D. Neither owners of K nor owners of A will benefit.
Question 18
these are the dataset: Earnings and…
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Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Total Residual 46 210,173,612.6150 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 9200.6014 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95% 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?
Consider the population regression of log earnings [Y;, where Y,= In(Earnings,;)] against two binary variables: whether a worker is married (D₁, where D₁;= 1 if the th person is married) and the worker's gender (D2;, where D₂;= 1 if the th person is female), and the product of the two binary variables Y₁ = Po+B₁D₁+P₂D2i + P3 (D₁¡ × D₂i) + Hi- The interaction term: O A. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. C. allows the population effect on log earnings of being married to depend on gender. D. cannot be estimated without the presence of a continuous variable.