3.Regressor(1)(2)#23X2X3X4#Intercept##R?0.600.63n110110In regression (1), Y is regressed against X1. In regression (2), Y is regressed against X1,X2, X3,and X4. Suppose the homoskedastic normal regression assumptions hold. What is thehomoskedasticity-only F-statistic for testing Ho: B2 = B3 = B4 = 0? (Round your answer to fourdecimal places.)Perform the significance test described in question #3. What do you conclude?reject Ho at a = 0.104.A)reject Ho at a = 0.05reject Ho at a = 0.01В)reject Ho at a = 0.10reject Ho at a = 0.05fail to reject Ho at a = 0.01C)reject Ho at a = 0.10fail to reject Họ at a = 0.05fail to reject Ho at a = 0.01D)fail to reject Ho at a = 0.10fail to reject Họ at a = 0.05fail to reject Ho at a = 0.01

Answered: In regression (1), Y is regressed…

MATLAB: An Introduction with Applications

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ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

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justify the reasons for the in1.Econometrics deals with the measurement of economic relationships which are stochastic or random. The simplest form of economic relationships between two variables X and Y can be represented by: ; where are regression parameters and the stochastic disturbance term Justify the reasons for the insertion of U-term in the modelsertion of of u (error term) in the model Y=a+bX+u?
Consider the fitted values from a simple linear regression model with intercept: yˆ = 5 + 6x. Assume that the total number of observations is n = 302. In addition, the R-squared of the regression is R2 = 0.6 and Pn i=1(xi − x¯) 2 = 15, where ¯x is the sample mean of x. Under the classical Gauss-Markov assumptions, a) What is the standard error of the estimated slope coefficient?
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
17) Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 41 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 0.9, 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 90000 and the sum of squared errors (SSE) is 10000. From this information, what is the number of degrees of freedom for the t-distribution used to compute critical values for hypothesis tests and confidence intervals for the individual…
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
Consider the fitted values from a simple linear regression model with intercept: yˆ = 5 + 6x. Assume that the total number of observations is n = 302. In addition, the R-squared of the regression is R2 = 0.6 and Pn i=1(xi − x¯) 2 = 15, where ¯x is the sample mean of x. Under the classical Gauss-Markov assumptions standard error of the estimated slope coefficient = 0.0365 value of the t-statistic for the null hypothesis H0 : the slope coefficient is equal to 0 versus H1 : the slope coefficient is different from 0 = 164.3836 What is the value of the F-statistic for the null hypothesis H0 : the slope coefficient is equal to 0 versus H1 : the slope coefficient is different from 0? Do the p-values of the t- and F-tests differ from each other? Justify your answer.
1. 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 0.4, standard error for the coefficient of z is 0.01, and the sample size is 20. Based on this information, find out whether labor cost is a statistically significant variable using an appropriate statistical test.
Consider the fitted values from a simple linear regression model with intercept: yˆ = 5 + 6x. Assume that the total number of observations is n = 302. In addition, the R-squared of the regression is R2 = 0.6 and Pn i=1(xi − x¯) 2 = 15, where ¯x is the sample mean of x. Under the classical Gauss-Markov assumptions standard error of the estimated slope coefficient = 0.0365 What is the value of the t-statistic for the null hypothesis H0 : the slope coefficient is equal to 0 versus H1 : the slope coefficient is different from 0?
Q3 Part (A) Where investment (I), government expenditure (G), and gross domestic product (Y) are measured in US$ billion and ̂u is the regression residualIs there any problem of Hetroscedasticity in above model? How do you know?
Consider the following simple linear regression model: y = β0 + β1x + u. Using a sample of n observations on x and y, you estimate the model by OLS and obtain the estimates βˆ 0, βˆ 1, and the R-squared of the regression, R2 . Then you scale this sample by a factor of 100, obtain a new sample {xi/100; yi/100} for i = 1, . . . , n, re-estimate the model by OLS, and denote the new coefficient estimates by β˜ 0, β˜ 1, and the new R-squared of the regression by R˜2 . a) Give the expression of β˜ 1 in terms of βˆ 1, and justify your answer.
Cascade Pharmaceuticals Company developed the following regression model, using time-series data from the past 33 quarters, for one of its nonprescription cold remedies: Y=−1.04+0.24X1−0.27X2�=−1.04+0.24�1−0.27�2 where Y� = quarterly sales (in thousands of cases) of the cold remedy X1�1 = Cascade's quarterly advertising (× $1,000) for the cold remedy X2�2 = competitors' advertising for similar products (× $10,000) Here is additional information concerning the regression model: sb1=0.042�b1=0.042, sb2=0.070�b2=0.070, R2=0.720�2=0.720, se=1.63��=1.63, F-statistic=31.402F-statistic=31.402, and Durbin-Watson (d) statistic=0.499Durbin-Watson (d) statistic=0.499. Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the cold remedy? (Hint: t0.05/2,33 - 3=2.042�0.05/2,33 - 3=2.042.) Check all that apply. X1�1 X2�2 What proportion of the total variation in sales is explained by the regression…
If in a multivariate regression model with significant F-value, all the estimated parameters have computed-t values less than 1, then the model is likely to have _____. Select one: a. partial correlation for each regressor is insignificant b. some or all the regressors are linearly dependent c. a problem of multicollinearity d. all of the above

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In regression (1), Y is regressed against X1. In regression (2), Y is regressed against X1,X2, X3, and X4. Suppose the homoskedastic normal regression assumptions hold. What is the homoskedasticity-only F-statistic for testing Ho: B2 = B3 = B4 = 0? (Round your answer to four decimal places.) %3D