Consider the following regression estimates (FN2) Linear regression wage belavg -1.063254 .3047845 abvavg .0693348 .3150202 female -2.751963 .2820787 married .9686236 .2612646 _cons 6.699098 .2889831 0 C. 5 Robust Coef. Std. Err. O d. 4 t Number of obs F(4, 1255) Prob > F R-squared Root MSE P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 = = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] .45606 6.132155 1,260 56.35 0.0000 0.1121 4.3987 assume that MLR 1-6 hold. In the regression above, how many coefficients (including the constant) are statistically significant at the 1% level? a. 3 O b. 1 -.4653108 .687359 -2.198565 1.481187 7.266042
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- Given the regression equationY = -50 + 12Xa. What is the change in Y when X changes by +3?b. What is the change in Y when X changes by -4?c. What is the predicted value of Y when X = 12?d. What is the predicted value of Y when X = 23?e. Does this equation prove that a change in X causes a change in Y?You estimated the following regression. What value would you predict for Y, if X = 56? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 340 -------------+---------------------------------- F(1, 338) = 14796.36 Model | 567720879 1 567720879 Prob > F = 0.0000 Residual | 12968703.6 338 38368.9455 R-squared = 0.9777 -------------+---------------------------------- Adj R-squared = 0.9776 Total | 580689583 339 1712948.62 Root MSE = 195.88 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 36.51691 .300204 121.64 0.000 35.92641 37.10741 _cons | 57.29142 22.39133 2.56 0.011…DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 10. Write the resulting regression equation. Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard.
- Consider the IV regression model Yi = β0 + β1Xi + β2Wi + ui, where Xi is correlated with ui and Zi is an instrument. Suppose that the first three assumptions in Key Concept (The IV Regression Assumptions) are satisfied. Which IV assumption is not satisfied whena) Zi is independent of (Yi, Xi, Wi)?b) Zi=Wi?c) Wi is1 for all i?d) Zi=Xi?You estimated a regression with the following output. Source | SS df MS Number of obs = 223 -------------+---------------------------------- F(1, 221) = 17592.99 Model | 182392130 1 182392130 Prob > F = 0.0000 Residual | 2291176.96 221 10367.3166 R-squared = 0.9876 -------------+---------------------------------- Adj R-squared = 0.9875 Total | 184683307 222 831906.786 Root MSE = 101.82 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 11.97037 .0902481 132.64 0.000 11.79252 12.14823 _cons | 74.40159 10.96696 6.78 0.000 52.78839 96.01479…Past class data has shown that the regression line relating the final exam score and the midterm exam score for students who take statistics from the College of Information Technology and Engineering from Dr. Kalaw is: final exam = 50 + 0.5 × midterm One interpretation of the slope is a. students only receive half as much credit (.5) for a correct answer on the final exam compared to a correct answer on the midterm exam. b. a student who scored 0 on the midterm would be predicted to score 50 on the final exam. c. a student who scored 10 points higher than another student on the midterm would be predicted to score 5 points higher than the other student on the final exam. d. a student who scored 0 on the final exam would be predicted to score 50 on the midterm exam.
- In the December, 1969, American Economic Review (pp. 886-896), Nathanial Leff reports thefollowing least squares regression results for a cross section study of the effect of age composition onsavings in 74 countries in 1964:log S/Y = 7.3439 + 0.1596 log Y/N + 0.0254 log G - 1.3520 log D1 - 0.3990 log D2 (R2= 0.57)log S/N = 8.7851 + 1.1486 log Y/N + 0.0265 log G - 1.3438 log D1 - 0.3966 log D2 (R2= 0.96)where S/Y = domestic savings ratio, S/N = per capita savings, Y/N = per capita income, D1 = percentage ofthe population under 15, D2 = percentage of the population over 64, and G = growth rate of per capitaincome. Are these results correct? Explain..Consider the regression model Yi = b0 + b1X1i + b2X2i + ui. Use approach 2from Section 7.3 to transform the regression so that you can use a t-statistic to testa. b1 = b2.b. b1 + 2b2 = 0.c. b1 + b2 = 1. (Hint: You must redefine the dependent variable in theregression.)consider a regression model Yi=B1+B2Xi+ui and you estimated B2hat =0.3. This implies that a unit change x is prdicted to
- Hello, please help me to solve the question (c) and (d) below.Consider this regression model (1) : Yt = β0 + β1 Ut + β2 Vt + β3 Wt + β4 Xt + εt ; where t= 1, ..., 75.We use OLS to estimate the parameters, producing the following model:Ŷt = 1.115 + 0.790 Ut − 0.327 Vt + 0.763 Wt + 0.456 Xt (0.405) (0.178) (0.088) (0.274) (0.017) Given that:R2 = 0.941; Durbin Watson stat DW = 1.907; RSS = 0.0757.(To answer the question, use the 5% level of significance, state clearly H0 and H1 that are tested, the test statistics that are used, and interpret the decisions.) (a) Describe the concepts of unbiasedness and efficiency. State the conditions required of regression (1) in order that the OLS estimators of the model parameters possess these properties. (b) Perform the following tests on the parameters of regression (1): (i) test whether the parameters β1, β2, β3 and β4 are individually statistically significant; (ii) test the overall significance of the regression model;…Suppose output (Q) is related to labor (L) and capital (K) in the following nonlinear way: Q = aLbKc When taking log to this equation, it is transformed into a linear LnQ = lna + b ln(L) + c Ln (K) One hundred twenty-three observations are used to obtain the following regression results: Dependant Variable: Q R-square F-ratio p-value on F Observations: 123 0.7547 184.56 0.00001 Variable Parameter Estimate Standard error t-ratio p-value Intercept 5.5215 0.9750 5.66 0.0001 L 0.650 0.2950 2.20 0.0295 K 0.350 0.1450 2.41 0.0173 A. Write the regression equation based on the output either in the transformed linear form or the original non-linear form. B. How do we interpret the coefficient of labor (L)? C. Is it statistical significance at the 1 percent level of significance?The following data relate the sales figures of restaurant, to the number of customers registered that week: Week Customers Sales (SR) First 16 330 Second 12 270 Third 18 380 Fourth 14 300 a) Perform a linear regression that relates bar sales to guests (not to time). b) If the forecast is for 20 guests next week, what are the sales expected to be?