A)
For β0, the P value from the table B is 0.0000 which is smaller than 0.05. In this case, the alternate hypothesis is accepted. The coefficient can't take the value of 0 in this regard.
Here, β0 < 0 is Accepted.
B)
For β1, the P value from the table B is 0.848 which is larger than 0.05. In this case, the null hypothesis is accepted. The coefficient can take the value of 0 in this regard.
C)
For β2 = β3 = β4, the P value from the table B for each of the coefficient are larger than 0.05 (0.848, 0.458, 0.129 respectively). In this case, the null hypothesis is accepted. The coefficients can take the value of 0 in this regard.
D)
For β0, the P value from the table B is 0.0000 which is smaller than 0.05. In this case, the alternate hypothesis is accepted. The coefficient can't take the value of 0 in this regard.
Here, β0 > 0 is Accepted..
End of the Solution...
Hrice-A' + β, ๒(asses) + ln(lotsize) + β3 ln(agrft) + β.bdmns + น where price: house price assess...
Please break down each part ( a, b, c and d) in detail. Thank you where price: house prioe; assess: the assessed housing value (before the house was sold); lotaize: size of the lot, in feet; agrft: square footage; and bdrms: number of bedrooms. The econometrician uses Stats 'reg' command, Le., uses OLS estimation, to get the following results: 5s df Mode1 6.13852904 16 Total8.01760352 87092156362 iprice Number of obs " FC 1, 6 280,94 Prob R-squared0.7656 Adj R-squared "...
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2. Use the data in hpricel.wfl uploaded on Moodle for this exercise. We assume that all assump- tions of the Classical Linear Model are satisfied for the model used in this question. (a) Estimate the model and report the results in the usual form, including the standard error of the regression. Obtain the predicted price when we plug in lotsize - 10, 000, sqrft - 2,300, and bdrms- 4; round this price to the nearest dollar. (b) Run a regression...
In the solution proposal DF = 21 when testing this hypotheses, but when doing a f test for significant regression DF is 24. I need help understanding this:) Regards Richard df MS Source I Number of obs 27 2. 24) - 200.25 - 0.0000 О. 9435 Adj R-squared 0.9388 .18837 2 7.10578187 Residual! .85163374 24.035484739 Model 14.2115637 Prob F R-squared Total 15.0631975 26.57935375 Root MSE Coef. Std. Err. [95% Conf. Interval] 125954 085346 .326782 1nLI.6029994 1nK I.3757102 cons1.170644 2.790.000 4.40...
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Interpret the coefficient on logged real gasoline price (lnP) in terms of the sign, magnitude and statistical significance. What does this estimate tell us about the average response of gasoline demand to changes in prices from 1975-1980. [lnGas = ln(gascap) lnP = ln(realprice) lnInc = ln(inccap) realprice = Real price of gasoline (2000 $) gascap = Gas demanded per capita (gallons per month) inccap = Real income per capita (2000 $) date = Year and month of observation (text) year...
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Based on the multiple regression model, does demand for beef respond significantly to price of pork? Why? df MS - - - - Source SS -----------+------- Model | 235.766738 Residual 57.3509099 ----------- ------- Total L 293.117648 3 13 78.5889127 4.41160845 Number of obs = EU3, 13) = Prob>F = R-squared = Adj R-squared = Root MSE = 17 17.81 0.0001 0.8043 0.7592 2.1004 - - - - - - - - - - - 16 18.319853 - - - -...