ENGR.ECONOMIC ANALYSIS
14th Edition
ISBN: 9780190931919
Author: NEWNAN
Publisher: Oxford University Press
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- 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…arrow_forward6) 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)arrow_forwardq11-arrow_forward
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