30. Consider the two regression models (i) y = Bo + B1X1 + B2 X2 + u (ii) y = Y0 + Y1Z1+ 72Z2 + v N(0,02) and where variables Z1 and Z, are distinct from X1 and X,. Assume u N(0, o?) and the models are estimated using ordinary least squares. If the true model is (i) then which of the following is true? A. Eļ3] = E[î1] = ß1 and Elô] = o B. E[ô] > o% C. E[ô] < o% D. None of the above as the two models cannot be compared
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- 31 - Find the regression model. Regresyon modelini bulunuz. Y X 2 5 5 9 7 4 4 11A) y=-3,21+2,11xB) y=2,56+5,43xC) y=5,27+0,11xConsider the following regression model Yt = β0 + β1 Ut + β2 Vt + β3 Wt + β4Xt + ∈t , where U, V, W, X and Y are economic variables observed from t = 1, . . . , 75, β0 , . . . , β4 are the model parameters and ∈t is the random disturbance term satisfying the classical assumptions. Ordinary Least Squares (OLS) is used to estimate the parameters, producing the following estimated model: Yt = 1.115 + 0.790*Ut − 0.327*Vt + 0.763*Wt + 0.456*Xt (0.405) (0.178) (0.088) (0.274) (0.017) where standard errors are given in parentheses, the R-squared = 0.941, the Durbin-Watson statistic is DW = 1.907 and the residual sum of squares is RSS = 0.0757. In answering this question, use the 5% level of significance for any hypothesis tests that you are asked to perform, state clearly the null and al- ternative hypotheses that you are testing, the test statistics that you are using and interpret the decisions that you make.…A “Cobb–Douglas” production function relates production (Q) to factorsof production, capital (K), labor (L), and raw materials (M), and an errorterm u using the equation Q = λKβ1Lβ2Mβ3eu, where λ, β1, β2, and β3 areproduction parameters. Suppose that you have data on production and thefactors of production from a random sample of firms with the same Cobb–Douglas production function. How would you use regression analysis toestimate the production parameters?
- 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.Which of the following are feasible equations of a least squares regression line for the annual population change of a small country from the year 2000 to the year 2015? Select all that apply. Select all that apply: yˆ=38,000+2500x yˆ=38,000−3500x yˆ=−38,000+2500x yˆ=38,000−1500xSuppose we want to predict job performance of mechanics based on mechanical aptitude test scores and test scores from personality test that measures conscientiousness. (a) Determine the regression equation. (b) Determine the SSE. Y X1 X2 1 40 25 2 45 20 1 38 30 3 50 30 2 48 28 3 55 30 3 53 34 4 55 36 4 58 32 3 40 34 5 55 38 3 48 28 3 45 30 2 55 36 4 60 34 5 60 38 5 60 42 5 65 38 4 50 34 3 58 38 Where Y is the Performance of the mechanics, X1 is the mechanical aptitude test and X2 is the personality test score that measure conscientiousness.
- Suppose we have collected a random sample from our population, denoted by (xi , yi), i = 1, . . . , n. We now fit a least squares line: yˆi = βˆ 0 + βˆ 1xi (i = 1, . . . , n). What additional assumption do we need in order to carry out statistical inference on our least square estimators βˆ 0 and βˆ 1? c. Using the results we’ve derived in class, prove that the sum of residuals is zero (Pn i=1 ei = 0)The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states, where xx is thousands of automatic weapons and yy is murders per 100,000 residents. xx 11.3 8.2 7.1 3.7 2.9 2.2 2.1 0.6 yy 13.9 10.7 10.3 7.2 6.5 5.6 5.5 4.6 Use your calculator to determine the equation of the regression line and write it in the y=ax+by=ax+b form. Round to 2 decimal places. According to this model, how many murders per 100,000 residents can be expected in a state with 4.6 thousand automatic weapons? Round to 3 decimal places. According to this model, how many murders per 100,000 residents can be expected in a state with 4.4 thousand automatic weapons? Round to 3 decimal places.Find the least-squares regression line y^=b0+b1xy^=b0+b1x through the points (−1,1),(1,9),(4,13),(9,19),(11,27),(−1,1),(1,9),(4,13),(9,19),(11,27), and then use it to find point estimates y^y^ corresponding to x=2x=2 and x=7x=7. For x=2x=2, y^y^ = For x=7x=7, y^y^ =
- Using 30 time series observations, the regression Y= B1 + B2 X + B3 Z + u is estimated and some results are reported as the following;Y't = 2.04 + 0.25 Xt – 0.12 Ztse (0.86) (0.08) (0.17)and the estimated first order autocorrelation coefficient (rho) P'= 0.92 b) Suppose you found the presence of 1st order autocorrelation problem in the errors, show how you would overcome this problem using GLS(Generalized Least Squares) (or feasible LS) estimation technique.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.Suppose we are given a least squares regression line ˆy= 4.3x +10. If a data point used to obtain the regression line is (1.5, 16) then the residual at that point is which of the following: (i) 0.45 (ii) -0.45 (iii) 0.55 (iv) 16.45