Consider the following regression estimates (FN2)Linear regressionNumber of obs1,260F(4, 1255)56.35Prob > F0.0000R-squaredRoot MSE0.11214.3987RobustwageCoef.Std. Err.P>|t|[95% Conf. Interval]belavgabvavg-1.063254.3047845-3.490.001-1.661197-.4653108.0693348.31502020.220.826-.5486894.687359female-2.751963.2820787-9.760.000-3.305-2.198565married.968623626126463.710.000456061.481187_cons6.699098.288983123.180.0006.1321557.266042wherewage in hourly wage in USS.belavg is a dummy variable (1 if below average looking O otherwise).abvavg is a dummy variable (1 if above average looking O otherwise), andmarried is a dummy variable (1 if married, O otherwise).What is the value of k in this regression?Only stote the number in your answer (e.g. don't write USS or 5).

From the given regression output, the F degrees of freedom is 4, 1255. That is, k=4,n-k-1=1255.

Answered: where • wage in hourly wage in US. •…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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10) The following results are from a regression where the dependent variable is COST OF COLLEGE and the independent variables are TYPE OF SCHOOL which is a dummy variable = 0 for public schools and = 1 for private schools, FIRST QUARTILE SAT which is the average score of students in the top quartile of SAT’s, THIRD QUARTILE SAT which is the average score of students in the 3rd quartile, and ROOM AND BOARD which is the cost of room and board at the school. The first set of results includes all the independent variables whereas the second set of results excludes the THIRD QUARTILE SAT variable. a) Based on these two sets of data, does there appear that multicollinearity is a problem (specifically, does it appear that THIRD QUARTILE SAT is highly collinear with the other independent variables? Explain. b) Calculate the VIF for THIRD QUARTILE SAT. c) Based on the VIF, do you think that multicollinearity is a problem? Explain.
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Illustrate the Regression Discontinuity Estimators?
Which of the following is FALSE? * The residuals in a regression model are assumed to have a zero mean. Data point below the regression line, the residual is negative. The residuals in a regression model are assumed to have increasing mean. The regression model assumes the residuals are normally distributed.
7.Why is the strength of the correlation between Y and X important in regression analysis?
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where • wage in hourly wage in US. • belavg is a dummy variable (1 if below average looking O otherwise) abvavg is a dummy variable (1 if above average looking O ocherwise), and • married is a dummy variable (1 if married. O ocherwise). What is the value of k in this regression?