QUESTION 41Consider the following regression estimates (FN4)Linear regressionNumber of obs29,471F(1, 29469)318.65Prob > F0.0000R-squared0.0108Root MSE801.34RobustincomeCoef.Std. Err.P>|t|(95% Conf. Interval]female-167.60869.389494-17.850.000-186.0124-149.2047_cons946.325615.6736960.380.000915.6045977.0468where income is weekly income in NZS, female is equal to 1 if the person if female and O if the person is male.What is the average income of all males in this regression?Please only state the number. Do not use the $ sign in your answer.

Answered: QUESTION 41 Consider the following…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The following is a partial computer output of a multiple regression analysis of a data set containing 20 sets of observations on the dependent variableThe regression equation isSALEPRIC = 1470 + 0.814 LANDVAL + 0.820 IMPROVAL + 13.5 AREA Predictor Coef SE Coef T P Constant 1470 5746 0.26 0.801 LANDVAL 0.8145 0.5122 1.59 0.131 IMPROVAL 0.8204 0.2112 3.88 0.0001 AREA 13.529 6.586 2.05 0.057 S = 79190.48 R-Sq = 89.7% R-Sq(adj) = 87.8% Analysis of Variance Source DF SS MS Regression 3 8779676741 2926558914 Residual Error 16 1003491259 62718204 Total 19 9783168000 For the problem above, we want to carry out the significance test about the coefficient of LANDVAL, what is the t-value for this test, and is it significant? 46.66, significant 2.05, significant 1.59, not significant 0.26, not significant
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Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here) SUMMARY OUTPUT Regression Statistics Multiple R 0.998041188 R Square 0.996086212 Adjusted R Square 0.994781616 Standard Error 917.1714273 Observations 5 ANOVA df SS MS F Significance F Regression 1 642276389.7 642276389.7 763.5208906 0.000104039 Residual 3 2523610.281 841203 427 Total 4 644800000 Coefficients Standard Errort Stat P-value Intercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639 X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876 What is variable cost per machine hour (rounded to the nearest cent)? OA. $6,679.62 OB. $917.17 OC. $9.80 OD. $.99 Lower 95% Upper 95.0% 9645.198517 10.92509577
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The parallel trends assumption is a crucial assumption for which the following estimators? Multiple regression Differences-in-Differences First differences Instrumental Variable
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2. You estimated a regression with the following output. Source | SS df MS Number of obs = 332 -------------+---------------------------------- F(1, 330) = 2.32 Model | 71599.1822 1 71599.1822 Prob > F = 0.1284 Residual | 10170207.7 330 30818.8111 R-squared = 0.0070 -------------+---------------------------------- Adj R-squared = 0.0040 Total | 10241806.9 331 30942.0147 Root MSE = 175.55 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | .7575183 .4969893 1.52 0.128 -.2201484 1.735185 _cons | 28.87757 42.80371 0.67 0.500 -55.32498 113.0801…

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