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- Please no written by hand and no emage Your company, which specializes in running shoes for men who are growing increasingly follicly-challenged (BalderDash®), has the following demand function: Q = a + bP + cM + dR where Q is the quantity demanded of BalderDash’s most popular shoes, P is the price of that product, M is consumer income, and R is the price of a related product. The regression results are: Adjusted R Square 0.7796 Independent Variables Coefficients Standard Error t Stat P-value Intercept 21,055.04 1428.27 14.74 8.1E-16 P -83.912 19.079 -4.398 0.000 M 0.0266 0.013 2.064 0.047 R -16.6 10.664 -1.556 0.129 Discuss whether you think these regression results will generate good sales estimates for BalderDash. Now assume that the income is $69,100, the price of the related good is $39, and BalderDash chooses to set the price of its product at $54. b. What is the estimated number of units sold given the data above? (round to nearest unit; no decimals) c.…4Please answer
- Calculate the correlation coefficient for the following ordered pairs. r= X y 8 6 5 9 26 2 2 D 11 5 (Round to three decimal places as needed.)"In the regression model InY=b0+b1*InX+u, the coefficient b1 is interpreted as" O the intercept O A covariance O A regressor O An elasticityYou estimated a regression with the following output. Source | SS df MS Number of obs = 411 -------------+---------------------------------- F(1, 409) = 4098.54 Model | 22574040.7 1 22574040.7 Prob > F = 0.0000 Residual | 2252702.97 409 5507.83122 R-squared = 0.9093 -------------+---------------------------------- Adj R-squared = 0.9090 Total | 24826743.7 410 60553.0334 Root MSE = 74.215 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.727341 .1050822 64.02 0.000 6.520772 6.933909 _cons | -.7552724 9.26027 -0.08 0.935 -18.95894 17.44839…
- An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.416) Suppose you have the following data on the price of orange and the quantity sold: Price per Pound (in Quantity Sold (in Dollars) Pounds) 0.50 0.75 1.00 1.25 1.50 10 7 699 5 2 Assume that the quantity sold (Y) is a linear function of the price (X), i.e. Y₁ =B₁ + B₂X₁ + ε₁ Estimate the population regression coefficients. (Do not use Computer)How responsive is the supply of cabbage to a change in the wage rate in agriculture?
- You are HR director for a growing architecture firm in Fort Lauderdale, Florida, which currently has need of drafting 20 blueprints every hour. Each of your company’s architects can create on average four blueprints per hour. You are considering hiring four drafters to shoulder the load; each drafter is slower than the architects and can create on average only two blueprints per hour. You scan the current wages in the Ft. Lauderdale area (https://www.bls.gov/oes/current/oessrcma.htm) and notice that the architects in your company earn the local occupational median wage of $30.14 per hour, but that the prospective four drafters will likely each want to get paid their local occupational median wage of $23.52 per hour. a. Would your company save money in the creation of the 20 blueprints by hiring the four new drafters and firing some architects? b. The Bureau of Labor Statistics projects that employment of drafters over the next decade will drop by 1.2%, compared to an increase of…You estimated a regression with the following output. Source | SS df MS Number of obs = 335 -------------+---------------------------------- F(1, 333) = 69555.83 Model | 211169628 1 211169628 Prob > F = 0.0000 Residual | 1010979.01 333 3035.97301 R-squared = 0.9952 -------------+---------------------------------- Adj R-squared = 0.9952 Total | 212180607 334 635271.28 Root MSE = 55.1 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 44.15183 .1674102 263.73 0.000 43.82251 44.48114 _cons | 31.63715 16.49849 1.92 0.056 -.8172452 64.09155…Consider the following regression estimates (FN2) Linear regression belavg abvavg female married _cons b. 1 wage O C. 5 O d. 4 Robust Coef. Std. Err. 3047845 3150202 2820787 -1.063254 .0693348 -2.751963 .9686236 .2612646 6.699098 .2889831 Number of obs F(4, 1255) Prob > F R-squared Root MSE t P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 |||||||||| = = .45606 6.132155 = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] 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 -.4653108 .687359 -2.198565 1.481187 7.266042
