wages = B1 + B2educ + ßzexper + ewhere wages denotes hourly wages. We estimate the regression in R and obtain the output## Coefficients:##Estimate Std. Error t value Pr(>|t|)## (Intercept)2.01.020.0455 *## educ0.50.510.3173## exper2.00.54 6.33e-05 ***## ---## Signif. codes:****' 0. 001 '**' 0.01**' 0.05 ' 0.1 ' ' 1Build 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)]

The coefficient of variable exper is β3. The estimated value of β3 is 2, i.e., β3 =2 The standard…

wages = ß1 + Bzeduc + Bzexper + e where wages denotes hourly wages. We estimate the regression in R and obtain the output ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) 0.0455 * 1 0.3173 ## (Intercept) 2.0 1.0 2 ## educ 0.5 0.5 ## exper 2.0 0.5 4 6.33e-05 *** ## --- ## Signif. codes: 0 '**** 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1 Build a 90% confidence interval for ß3z using a normal approximation. (Use that if Z ~ N(0, 1) and z1-a satisfies P(Z > z1-q) = a, then zo,9 = 1.28, zo.95 = 1.64, Z0,975 = 1.96, Z0,.99 = 2.33, and z0.995 = 2.58). O a. [2 – 1.64 × (0.5), 2 + 1.64 × (0.5)] O b. [2 – 1.28 × (0.5), 2 + 1.28 × (0.5)]

wages = ß1 + Bzeduc + Bzexper + e where wages denotes hourly wages. We estimate the regression in R and obtain the output ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) 0.0455 * 1 0.3173 ## (Intercept) 2.0 1.0 2 ## educ 0.5 0.5 ## exper 2.0 0.5 4 6.33e-05 * ## --- ## Signif. codes: 0 ' 0.001 '' 0.01 '*' 0.05 '.' 0.1 '' 1 Build a 90% confidence interval for ß3z using a normal approximation. (Use that if Z ~ N(0, 1) and z1-a satisfies P(Z > z1-q) = a, then zo,9 = 1.28, zo.95 = 1.64, Z0,975 = 1.96, Z0,.99 = 2.33, and z0.995 = 2.58). O a. [2 – 1.64 × (0.5), 2 + 1.64 × (0.5)] O b. [2 – 1.28 × (0.5), 2 + 1.28 × (0.5)]

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter7: Production Economics

Section: Chapter Questions

Problem 1.3CE

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Consider the following regression estimates (FN2) Linear regression belavg abvavg female married _cons b. 1 wage O C. 5 O d. 4 Robust Coef. Std. Err. 3047845 3150202 2820787 -1.063254 .0693348 -2.751963 .9686236 .2612646 6.699098 .2889831 Number of obs F(4, 1255) Prob > F R-squared Root MSE t P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 |||||||||| = = .45606 6.132155 = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] 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 -.4653108 .687359 -2.198565 1.481187 7.266042
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 Residual 46 210,173,612.6150 Total 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 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95 % 9200.6014 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 1 of 2: What would be your expected salary with no education and no experience?
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?
Suppose that a researcher, using wage per hour data on 250 randomly selected male workers and 280 female workers, estimates the following OLS regression wage - 12.68+2.79xMale (0.18) (0.84) R = 0.06, s-3.10 where Male is a dummy variable that takes the value 1 if the worker is male and 0 if female: s represents the standard error of the regression and in brackets homoskedastic std errors are reported. The researcher wants to find the gender pay gap as percentage bf the wagé per hour of women. According to this information the gender pay gap on average against women is approximately -22% -25% -38% O-16%
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You estimated the following regression. What value would you predict for Y, if X = 42? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 303 -------------+---------------------------------- F(1, 301) = 52790.25 Model | 510753802 1 510753802 Prob > F = 0.0000 Residual | 2912221.36 301 9675.15401 R-squared = 0.9943 -------------+---------------------------------- Adj R-squared = 0.9943 Total | 513666023 302 1700880.87 Root MSE = 98.362 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 17.14603 .0746254 229.76 0.000 16.99918 17.29289 _cons | 56.37208 8.600915 6.55 0.000…
In the Managerial Solution, we estimated a focus group's demand curve for iTunes downloads. The estimated coefficient on price was-413, and the 1-statistic was -12.8. nage d coe ndard Using these values, what is the standard error of this estimated coefficient? est The standard error of the price coefficient is (Enter your response rounded to two decimal places) Suppose we had another focus group sample, ran a regression on that sample, and obtained the same coefficient on price but with a standard error five times as large What can you say about the statistical significance of the price coefficient in this second sample? rt A The price coefficient would be statistically significantly different than zero at the 0.05 confidence level Sulag 50,00 t-Val Stan would be would not be