The table below summarizes results for an estimated log-linear demand function:SUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegressionResidualTotalInterceptIn (Price)df0.9620.9250.9230.05930128 0.097363103 0.003477254291.303418073CoefficientsSSMSFSignificance F1.20605497 1.20605497 346.84124 2.60934E-174.71-1.57Standard Error0.180.08t Stat26.29-18.62P-value0.000.00Lower 95%4.34-1.75How much should this company adjust their price to obtain a 10% increase in revenue?raise price by 17.54%lower price by 17.54%raise price by 0.1754%lower price by 0.1754%Upper 95%5.07-1.40

The revenue could be raised in case of elastic demand , by lowering the price of the good. On the…

SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept In (Price) df 0.962 0.925 0.923 0.059 30 SS MS F 1.20605497 1.20605497 346.84124 1 28 0.097363103 0.003477254 29 1.303418073 Coefficients Standard Error 0.18 0.08 4.71 -1.57 t Stat 26.29 -18.62 P-value 0.00 0.00 Significance F 2.60934E-17 Lower 95% 4.34 -1.75 How much should this company adjust their price to obtain a 10% increase in revenue? raise price by 17.54% lower price by 17.54% raise price by 0.1754% lower price by 0.1754% Upper 95% 5.07 -1.40

SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept In (Price) df 0.962 0.925 0.923 0.059 30 SS MS F 1.20605497 1.20605497 346.84124 1 28 0.097363103 0.003477254 29 1.303418073 Coefficients Standard Error 0.18 0.08 4.71 -1.57 t Stat 26.29 -18.62 P-value 0.00 0.00 Significance F 2.60934E-17 Lower 95% 4.34 -1.75 How much should this company adjust their price to obtain a 10% increase in revenue? raise price by 17.54% lower price by 17.54% raise price by 0.1754% lower price by 0.1754% Upper 95% 5.07 -1.40

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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5.2 42 The following regression shows the impact of the number of rooms (ROOM) on the monthly rent of an apartment (in thousand Rands) (RENT) from a sample of 42 randomly chosen apartments in Parow: Dependent Variable: RENT Variable C ROOM r-square: 0.7180 Parameter -0.7705 2.1331 SE of regression: 2.3397 Std. Error 0.8233 0.2114 sample size = 42 What will be values of the parameters, variance and standard errors of the parameters, estimated variance of error terms, coefficient of determination and correlation coefficient, if RENT is measured in Rands and there is no change to the unit of measurement of the ROOM variable?
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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 0.41
QUESTION 10 Answer questions 10 to 16 based on the regression outputs given in Table 1& 2. Table 1 DATA4-1: Data on single family homes in University City community of San Diego, in 1990. price - sale price in thousands of dollars (Range 199. 9 505) sqft - square feet of living area (Range 1065 - 3000) Table 2 Model 1: OLS, using observations 1-14 Dependent variable: price coefficient std. error t-ratio p-value 52. 3509 0.138750 37. 2855 0.0187329 0. 1857 8. 20e-06 *** const sqft 7. 407 Me dependent var Sun squared resid R-squared F(1, 12) Log-likelihood Schwarz criterion 317. 4929 18273. 57 0. 820522 54. 86051 -70. 08421 145. 4465 Hannan-Quinn S.D. dependent var S.E. of regression Adjusted R-squared P-value (F) Akaike criterion 88. 49816 39. 02304 0. 805565 8. 20e-06 144. 1684 144. 0501 There are observations included in this dataset. It is a. data. O 12; cross-sectional 13; time-series data 14; cross-sectional In this regression model, sale price of a single-family house is the. the…
6) 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)
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The dependent variable in the regression in our cost driver analysis is which of the following? Company sales Total overhead cost for the entire period of time Total overhead cost per month
18 Calculate the least square regression líne equation with the given X and Y values. Consider the values: X Y 60 3.1 61 3.6 62 3.8 63 4 65 4.1 To Find, Least Square Regression Line EquationY = a+ b X
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…
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QUESTION 2 Consider the following bivariate linear regression model y = a+3x+u. Suppose that E[u]x] #0 and that z is a valid instrument for r. Knowing that Cov(y, z) = 0.5 and Cov(z, x) = 0.5, the IV estimate of 3 is 1. %3D O True O False
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Total Residual 46 210,173,612.6150 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 9200.6014 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95% 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?