The following two equations were estimated using the data in MEAPSINGLE. The key explanatoryvariable is lexppp, the log of expenditures per student at the school level.math4 = 24.49 + 9.01 lexpp – 422 free – .752 Imedinc – .274 pctsgle(.071)(59.24) (4.04)n = 229, R² = .472, R²(5.358)(.161)462.149.38 + 1.93 lexppp – .060 free – 10.78 Imedinc – .397 pctsgle + .667 read4(3.76)math4(41.70) (2.82)(.054)(.111)(.042)n = 229, R = .749, R = .743.(i) If you are a policy maker trying to estimate the causal effect of per-student spending on mathtest performance, explain why the first equation is more relevant than the second. What is theestimated effect of a 10% increase in expenditures per student?(ii) Does adding read4 to the regression have strange effects on coefficients and statistical signifi-cance other than Brexppp?(iii) How would you explain to someone with only basic knowledge of regression why, in this case,you prefer the equation with the smaller adjusted R-squared?

The two estimated multiple regression equations in the question analyse the factor causing the…

Answered: The following two equations were…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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X(t) is a wide sense stationary stochastic process with autocorrelation function sin(271000r) R„(T)=10- 271000r The process Y(t) is a version of X(t) delayed by 50 micro seconds. {Y(t) = X(t - t0)} (a) Derive the autocorrelation function of Y (t). (b) Derive the cross-correlation function of X(t) and Y (t). (c) Are X(t) and Y (t) jointly wide sense stationary? IUnloaded File Details
The assumption that is required to shows the efficiency of the OLS estimator, consistency and unbiasedness is: i) Cov(u,,u-;) { A0 ii) Var(u,) =o? iii) E(u,) = 0 iv) u, ~ N(0,0²) a) (i), (ii) and (iii) only b) (i) and (iii) only c) (ii) and (iv) only d) (i), (ii) (iii) and (iv) Answer O A OD
Advertising A small manufacturing firm collected the following data on advertising expenditures A (in thousands of dollars) and total revenue R (in thousands of dollars). (a) Draw a scatter diagram of the data. Comment on the type of relation that may exist between the two variables (b) The quadratic function of best fit to these data is R(A) = - 7.76A2 + 411.88A + 942.72 Use this function to determine the optimal level of advertising. (c) Use the function to predict the total revenue when the optimal level of advertising is spent. (d) Use a graphing utility to verify that the function given in part (b) is the quadratic function of best fit. (e) Use a graphing utility to draw a scatter diagram of the data, and then graph the quadratic function of best fit on the scatter diagram.
Solve for x for the following equation: 4ln(3x-8) = 60
All questions utilize the multivariate demand function for Smooth Sailing sailboats in C6 on text page 83. Compute to three decimal places. Initial values are: PX = $9500 PY = $10000 I = $15000 A = $170000 W = 160 This function is: Qs = 89830 -40PS +20PX +15PY +2I +.001A +10W 1.(a). Use the above to calculate the arc price elasticity of demand between PS = $9000 decreasing to PS = $8000. The arc elasticity formula is: 1.(b). Judging from the computation in (a), do you expect the revenue resulting from the decrease in Ps to $8000 to increase, remain the same, or decrease relative to the revenue at Ps = $9000. (Hint: see the table on page 65 of Truett). Explain your choice. 1.(c). Calculate the point elasticity of demand for Smooth Sailing sailboats at PS = $9000 (which should make Qs = 101600). The formula is: 1.(d). Does this elasticity value indicate that Smooth Sailing demand is relatively responsive to changes in the price of these sailboats? Explain…
I need help especifically with the part where they rearrange the equation as: .0790(V/E) = .11 + .058(.75)(D/E). How do they get an inverse (V/E) on the left side without the .11. And how do they get a (D/E) ratio.
No tables, ONLY FORMULAS, PLEASE Correct answers; (i) ?^(2) =0.060602?(1/2)a, (ii) ?^(1/3) = 0.14359??, (iii) ?^(12) = 0.11881??, (iv) ? = 0.23881?2?
Consider the output here from a regression in R. What is 3₂? Coefficients: Estimate (Intercept) 1.708 5.404 -1.478 9.531 X1 X2 X3 Std. Error 0.555 2.792 0.6 2.758
The regression equation Netincome = 2,049 +.0478 Revenue was estimated from a sample of 100 leading world companies (variables are in millions of dollars). (0-1) If Revenue = 1, then Netincome = (a-2) Choose the correct statement. (b) Choose the right option. (c) O Increasing the revenue raises the net income. Decreasing the revenue raises the net income. O Increasing the revenue lowers the net income. million. (Round your answer to 2 decimal places.) O The intercept is not meaningful because a firm cannot have net income when revenue is zero. The intercept is meaningful because a firm can have net income when revenue is zero. If Revenue = 24,574, then NetIncome = million. (Round your answer to the nearest whole number.)
The average remaining lifetimes for women of various ages in a certain country are given in the following table. (A graphing calculator is recommended.) Average Remaining Lifetimes for Women Age (x) Years (y) 80.6 15 65.5 35 46.9 65 19.1 75 12.8 (a) Find the equation of the least-squares line for the data. (Round all numerical values to two decimal places.) (b) Use the equation from part (a) to estimate the remaining lifetime of a woman of age 19. (Round your answer to the nearest year.) yr (c) Is the procedure in part (b) an example of interpolation or extrapolation? O interpolation O extrapolation
u (x1, x2) = x1x2 What is the value of MRS(4, 20)? (Don't forget to include a minus sign in your answer)
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