ECON ASSINGMENT 3

ECON ASSINGMENT 3 - Question 1 Model 1 OLS estimates using...

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Question 1: Model 1: OLS estimates using the 832 observations 1-832 Dependent variable: price Coefficent Std. Error t-ratio p-value const 116.562 2.36649 49.2553 <0.00001 *** lot 1.38523 0.208863 6.6323 <0.00001 ** SSUM 5.11552 3.04493 1.6800 0.09333 * Mean dependent variable 122.0756 S.D dependent var 44.34779 Sum of squared residuals 1546626 S.E of regression 43.19320 R-squared 0.053675 Adjusted R-squared 0.051392 F(2,829) 23.51038 P-Value (F) 1.17e- 10 Log Likelihood -4312.102 Akaike criterion 8630.205 Schwarz criterion 8644.376 Hannan-Quinn 8635.639 Price i = 116.562 + 1.38523LOT i + 5.11552SSUM i R 2 = 0.053675 SER = 43.1932 (2.36649) (0.208863) (3.04493) Interpreting the LOT coefficient: A 1 unit (i.e. 1 acre) increase in lot size will lead to a 1.38523 (i.e. $1,385.23) increase in price controlling for OTHER VARIABLES. Interpreting the SSUM coefficient: The mean price of a house sold in spring/summer is 5.11552 units (i.e. $5,115.52), higher than that for a house sold in fall/winter, controlling for lot size. Question 2: H o : β 2 = 0 H 1 : β 2 > 0 (RIGHT SIDE TEST) t Statistic = 1.680 t Critical = 1.645 Since the t-ratio for β 2 (1.680) is less than the 5 percent critical value (1.645), we reject the null hypothesis (H o : β 2 = 0). Given that the coefficient is significant, it seems reasonable to conclude that the mean price of residential houses sold in Prince George is spring/summer is
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significantly higher than that of houses sold in Fall/winter, controlling for lot size. β 2 is significant and greater than zero. Question 3: P-VALUE METHOD P-value = P (-1.68) = 0.0465 (For a 1-sided test) Since the level of significance (0.05) is greater than the p-value (0.0468), the null hypothesis is rejected (H o : β 2 = 0). SAME CONCLUSION AS IN QUESTION 2. Question 4:
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ECON ASSINGMENT 3 - Question 1 Model 1 OLS estimates using...

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