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- 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…How does the particle used in FastSLAM differ from the one used in Monte Carlo localization?What is the value of Y if C = 200, a = 20, and b = 0.9?
- π = 10kl - k²1² +1 dk 5 dn dk - dπ dk dπ dk Find and dπ dl dπ = : 100 - 10kl + and = 1012kl² and = 10l-2kl² and dl dπ dl dπ dl dπ = = 100-10lk² 10k 2lk²+1 - = 10k - 2lk²Solve for x for the following equation: 4ln(3x-8) = 60The height (in inches) of a random sample of 28 players in NCAA Division 1 basketball teams is shown to the left. x 75 80 75 87 77 77 67 83 77 80 73 82 73 71 80 76 72 78 68 74 74 72 76 86 77 77 78 5 Sum of squared x minus n times squared mean: ∑x² − nx̅² =_____ a 590.74 b 582.01 c 573.41 d 564.94
- Use General Linear Proce ss to determine the Emean functon and ARC2) given the autocovariance function of ky XE 七ート 2ヒー24. The estimation of the model with quarterly car sales in the U.S. from 1975 to 1990 gives: Source | df MS Number of obs = 64 F( 2, Prob > F 61) = 12.21 Model .32720224 2 .16360112 0.0000 Residual | .817286587 61 .013398141 R-squared Adj R-squared = 0.2625 Root MSE 0.2859 Total | 1.14448883 63 .018166489 .11575 lqne | cCoef. t P>|t| std. Err. [95% Conf. Interval] 1price lincome -.4604611 3.37186 6.89398 -.8280926 .1838504 -4.50 0.000 -1.195724 2.399991 . 4860261 4.94 0.000 1.428121 _cons 5.92543 .4843662 12.23 0.000 4.95688 Based on the parameter estimates, what is the predicted effect of a 10% increase in price on the number of cars sold? What would be the effect of that price increase on the value of car sales?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