You run the regressiony₁ = B₁ + B₁ x ₁₁ + B³ 2 x2iВо1 1i+ εi•variance of ε turns out to beiTheproportional to 4x. Write down a1itransformed, homoskedasticequation.

Now, the transformed equation with homoskedasticity is:yi=β0+β1x1i+β2x2i+2xi∗ϵi∗ This…

Answered: You run the regression y₁ = B₁ + B₁ x…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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You estimated a regression with the following output. Source | SS df MS Number of obs = 289 -------------+---------------------------------- F(1, 287) = 41986.64 Model | 664544048 1 664544048 Prob > F = 0.0000 Residual | 4542496.25 287 15827.5131 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 669086544 288 2323217.17 Root MSE = 125.81 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 43.81013 .2138056 204.91 0.000 43.38931 44.23096 _cons | 49.31707 16.96222 2.91 0.004 15.93094 82.70319…
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You estimated a regression with the following output. Source | SS df MS Number of obs = 411 -------------+---------------------------------- F(1, 409) = 4098.54 Model | 22574040.7 1 22574040.7 Prob > F = 0.0000 Residual | 2252702.97 409 5507.83122 R-squared = 0.9093 -------------+---------------------------------- Adj R-squared = 0.9090 Total | 24826743.7 410 60553.0334 Root MSE = 74.215 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.727341 .1050822 64.02 0.000 6.520772 6.933909 _cons | -.7552724 9.26027 -0.08 0.935 -18.95894 17.44839…
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When running a ols regression, if my control variables are insignificant via T-test should I keep them in the regression? Are they significant?
You estimated a regression with the following output. Source | SS df MS Number of obs = 335 -------------+---------------------------------- F(1, 333) = 69555.83 Model | 211169628 1 211169628 Prob > F = 0.0000 Residual | 1010979.01 333 3035.97301 R-squared = 0.9952 -------------+---------------------------------- Adj R-squared = 0.9952 Total | 212180607 334 635271.28 Root MSE = 55.1 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 44.15183 .1674102 263.73 0.000 43.82251 44.48114 _cons | 31.63715 16.49849 1.92 0.056 -.8172452 64.09155…
Q1
If the model y = ßo + ß1x1 + ß2x2 + v satisfies the first Gauss-Markov assumptions, then v has: a. a zero mean and is correlated with only x1 b. C. a zero mean and is correlated with x₁ and x2 a zero mean and is correlated with only x2 d. a zero mean and is uncorrelated with X1 and X2 a. a O b. b О с. C ○ d. d
Chegg Home Expert Q&A My solutions Student question Time Left: 00:09:16 Use the data set Home Morloage Disdosure Act Data (HMDA) from the AER package. Subtract 1 from the factor "Was the mortage denied?". Then, eatimate the Inear regression model of paymentsto - income rato on the factor "Was the mortage denied?" ising Ols. Based on these results, the prebabity a mertougo will be denled if the payment - to - income ratio is equal to 2 is a. 1.13 b. 0.107 c. None of these d. 0.52 e. 1.00 Use the data set Home Mortgage Disclosure Act Data (HMDA) from the AER package. Subtract 1 from the factor "Was the mortage denied?". Then, estimate the linear regression model of payments-to-income ratio on the factor "Was the mortage denied?" using OLS. Based on these results, the probability a mortgage will be denied if the payment-to-income ratio is equal to 2 is a. 1.13 Ob.0.87 Oc None of these d.0.52 1.00
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?
You estimated a regression with the following output. Source | SS df MS Number of obs = 268 -------------+---------------------------------- F(1, 266) = 23.48 Model | 668419.175 1 668419.175 Prob > F = 0.0000 Residual | 7572666.51 266 28468.6711 R-squared = 0.0811 -------------+---------------------------------- Adj R-squared = 0.0777 Total | 8241085.68 267 30865.4895 Root MSE = 168.73 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 1.014128 .2092916 4.85 0.000 .6020489 1.426207 _cons | 9.173163 21.13463 0.43 0.665 -32.43929 50.78561…

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You run the regression y₁ = B₁ + B₁ x ₁₁ + B³ 2 x2i Во 1 1i + ε i • variance of ε turns out to be i The proportional to 4x . Write down a 1i transformed, homoskedastic equation.