11. For the model Yi = Bo + B1x1li + B2x2i + Ui You obtain the fitted residuals and run the regression û; = a0 + a1xli + a2X2i + a3X3i + a4x4i + e; Unfortunately the only information you have retained is the number of observations (341) and the R2 of the auxiliary regression (0.15). What can you conclude about the joint significance of the parameters on x3 and x4 in a model of y which also includes x1 and x2 (at a 1% significance level)? A. Nothing - there is not enough data to do an F-test B. The parameters are jointly significant C. One of the parameters is significant D. The original regression suffers from Heteroskedasticity E. The parameters are both positive

11. For the model Yi = Bo + B1x1li + B2x2i + Ui You obtain the fitted residuals and run the regression û; = a0 + a1xli + a2X2i + a3X3i + a4x4i + e; Unfortunately the only information you have retained is the number of observations (341) and the R2 of the auxiliary regression (0.15). What can you conclude about the joint significance of the parameters on x3 and x4 in a model of y which also includes x1 and x2 (at a 1% significance level)? A. Nothing - there is not enough data to do an F-test B. The parameters are jointly significant C. One of the parameters is significant D. The original regression suffers from Heteroskedasticity E. The parameters are both positive

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

In running a regression of the retunrs of stock XYZ against the returns on the market, the Std for the returns of stock XYZ is 20% and that of the market returns is 15%. If the estimated beta is found to be 0.75 : What is the maximum possible value of beta given that the standar deivation of the returns of stock XYZ is 20% and those of the market is 15% ?
What is the significance of R and R2 in gression model?
8)Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 11 members of the population, where for each member, the value of Y as well as the values of the three explanatory unknowns were observed. The data is entered into a computer using linear regression software and the output summary tells us that R-square is 0.86, the linear model coefficient of the first explanatory unknown is 7 with standard error estimate 2.5, the coefficient for the second explanatory unknown is 11 with standard error 2, and the coefficient for the third explanatory unknown is 15 with standard error 4. The regression intercept is reported as 28. The sum of squares in regression (SSR) is reported as 86000 and the sum of squared errors (SSE) is 14000. From this information, what is MSE/MST? .5000 NONE OF THE OTHERS .2000 .3000 .4000
Suppose that the sales of a company (Y) is regressed on advertising expenditure (x) and labor cost (z), and the estimated regression equation is Y = 5 + 0.5x + 0.7z + u (where u is the error term). Here, sales, advertising expenditure and labor cost are measured in million Tk. Standard error for the coefficient of x is 0.04, standard error for the coefficient of z is 0.01, and the sample size is 20. Can we conclude that advertising expenditure is a statistically significant variable?
A set of paired data has a least squares regressionline with equation yn = 0.50x + 2.0 and a correlationcoefficient of r = 0.80. Suppose we convert the datafor each variable to z-scores and then compute the newregression line. What will the equation be?A) zˆy = 0.50zx B) zˆy = 0.64zxC) zˆy = 0.80zx D) zˆy = 0.50zx + 20E) zˆy = 0.80zx + 20
I have some doubts regarding linear regression. if any 2 variables in X1, X2 AND Y have a positive correlation, then in the linear regression Y = b0 + b1X1 +b2X2 +e, will the sign of b1 and b2 both be positive? will the residuals that we get from linear regression will always be uncorrelated given X?
Suppose that a researcher, using wage data on 200 randomly selected male workers and 240female workers, estimates the OLS regressionW age ˆ =10(0.2)+2(0.4)× M ale, R2 = 0.10, SER = 4,where Wage is measured in dollars per hour and Male is a binary variable that is equal to 1 if theperson is a male and 0 if the person is a female. Define the wage gender gap as the difference inmean earnings between men and women.1. What is the estimated gender gap?2. Is the estimated gender gap significantly different from 0? (Compute the p-value for testingthe null hypothesis that there is no gender gap.)13. Construct a 95% confidence interval for the gender gap.4. In the sample, what is the mean wage of women? Of men?5. Another researcher uses these same data but regresses Wages on Female, a variable that isequal to 1 if the person is female and 0 if the person a male. What are the regression estimatescalculated from this regression?W age ˆ = _ + _ × F emale, R2 = _, SER = _.
9)Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 11 members of the population, where for each member, the value of Y as well as the values of the three explanatory unknowns were observed. The data is entered into a computer using linear regression software and the output summary tells us that R-square is 0.79, the linear model coefficient of the first explanatory unknown is 7 with standard error estimate 2.5, the coefficient for the second explanatory unknown is 11 with standard error 2, and the coefficient for the third explanatory unknown is 15 with standard error 4. The regression intercept is reported as 28. The sum of squares in regression (SSR) is reported as 79000 and the sum of squared errors (SSE) is 21000. From this information, what is the adjusted R-square? .8 .7 NONE OF THE OTHERS .6 .5
If the points (x1, y1), (x2, y2),..., (xn, yn) lie on a straight line, what can you say about the regression line associated with these points?
Suppose the regression in Equation is estimated using LoSTR andLoEL in place of HiSTR and HiEL, where LoSTR = 1 - HiSTR is anindicator for a low-class-size district and LoEL = 1 - HiEL is an indicatorfor a district with a low percentage of English learners. What are thevalues of the estimated regression coefficients?
There is a linear regression: Yi = B0+ B1(Xi^2)+ ei present, where Xi is squared. ei ∼ N(0,σ2). How would I derive LSE for B0 and B1 and their variance?
The coefficients in a distributed lag regression of Y on X and its lags can be interpreted as the dynamic causal effects when the time path of X is determined randomly and independently of other factors that influence Y. Explain How?