Please help me (2.1) We first estimate the following model (you may assume all of the OLS assumptionshold):Iprice; = Bo + Bịcrime; + Balnox; + Bzldist; + Barooms; + Bgstratio; + u;OLS results (robust standard errors in parentheses):Īprice= 11.005 – 0.015 · crime – 0.901 · Inox – 0.215 · Idist +0.247 · rooms(0.368)(0.002)(0.122)(0.052)(0.024)0.042 · stratio(0.004)n = 506, R² = 0.656, Ř² = 0.653For each of the slope coefficient estimates, discuss whether they have the expectedsign. If a slope coefficient estimate does not have the expected sign, briefly explainwhat might be going on (no calculations are necessary).(2.2) Using the results from (2.1), carry out two-sided hypothesis tests on the estimates ofBị and 32 (at the 1% level). Interpret these coefficient estimates.(2.3) We next estimate the following model (you may assume all of the OLS assumptionshold):lprice;Bo + Bicrime; + Balnox; + Bzldist; + Barooms; + Bzrooms? +Bostratio; + uiOLS results (robust standard errors in parentheses):Īprice= 13.656 – 0.016 · crime – 0.839 - Inox – 0.164 · Idist – 0.676 · rooms +(0.786)(0.003)(0.123)(0.053)(0.244)0.072 - rooms? – 0.036 - stratio(0.019)(0.005)506, R2 = 0.681, R² = 0.677n =Does the coefficient estimate on rooms have the expected sign? Problem 2. More on housing prices.For a sample of 506 communities in the Seattle area, we examine the effect the crime rateand number of rooms have on the median price of houses.Here is a description of the variables in the data set:Variable Descriptionpricemedian housing price, $crimes committed per capitanitrous oxide concentration (measure of pollution)average number of roomsweighted distance to 5 employment centersaverage student-teacher ratiolog(price)log(nox)log (dist)square of roomscrimenoxroomsdiststratioIpriceInoxldistrooms2rooms3cube of roomsSample summarypricecrimenoxroomsdiststratioIpriceInoxIdistrooms2rooms3count506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000mean 22511.5098813.6115365.5497836.2840513.79575118.4592899.9410571.6930911.188233 39.981964 257.579839std9208.8561718.5902471.1583950.7025942.1061372.1658200.4092550.2014100.5395019.07857190.472442min5000.0000000.0060003.8500003.5600001.13000012.6000008.5171931.3480730.12221812.67359945.11801225% 16850.0000000.0820004.4900005.8825002.10000017.4000009.7320931.5018530.74193734.603826 203.55722650% 21200.0000000.2565005.3800006.2100003.21000019.1000009.9617571.6826881.166266 38.564102 239.48306175% 24999.000003.6770006.2400006.6200005.18750020.20000110.1265911.8309801.64622343.824398 290.117515max 50001.000000 88.9759988.7100008.78000012.13000022.00000010.8197982.1644722.49568277.088394 676.836083Sample correlation matrixpricecrimediststratioIpriceInoxIdistrooms2rooms3noxroomsprice 1.000000 -0.387919 -0.4260370.6957800.249339 -0.5033400.953320 -0.4294470.2910630.7186160.730700crime -0.3879191.0000000.421152 -0.218816 -0.3799090.288691 -0.5274950.429639 -0.464472 -0.203314 -0.187702nox -0.4260370.4211521.000000 -0.302828 -0.7702220.186863 -0.5087670.993943 -0.832844 -0.284613 -0.263683rooms 0.695780 -0.218816 -0.3028281.0000000.205410 -0.3540080.632909 -0.3049880.2565890.9945300.979986dist0.249339 -0.379909 -0.7702220.2054101.000000 -0.2292690.342008 -0.8087950.9646770.1838750.161220stratio -0.5033400.2886910.186863 -0.354008 -0.2292691.000000 -0.4976350.227910 -0.234807 -0.364044 -0.369048Iprice 0.953320 -0.527495 -0.5087670.6329090.342008 -0.4976351.000000 -0.5133710.4041310.6430870.643853Inox -0.4294470.4296390.993943 -0.304988 -0.8087950.227910 -0.5133711.000000 -0.860750 -0.286794 -0.265755Idist0.291063 -0.464472 -0.8328440.2565890.964677 -0.2348070.404131 -0.8607501.0000000.2299220.202454rooms20.718616 -0.203314 -0.2846130.9945300.183875 -0.3640440.643087 -0.2867940.2299221.0000000.995355rooms30.730700 -0.187702 -0.2636830.9799860.161220 -0.3690480.643853 -0.2657550.2024540.9953551.000000

Consider X1, X2,…, and Xn as the independent variable with Y as the dependent variable. Then, the…

(2.1) We first estimate the following model (you may assume all of the OLS assumptions hold): Iprice; Bo + Bicrime; + B₂lnox; + Bildist; + Barooms; +35stratio; + ui OLS results (robust standard errors in parentheses): Iprice=11.005 -0.015 crime - 0.901 Inox -0.215 ldist +0.247-rooms- (0.002) (0.052) (0.024) (0.122) (0.368) 0.042-stratio (0.004) n = 506, R² = 0.656, R² = 0.653 For each of the slope coefficient estimates, discuss whether they have the expected sign. If a slope coefficient estimate does not have the expected sign, briefly explain what might be going on (no calculations are necessary). (2.2) Using the results from (2.1), carry out two-sided hypothesis tests on the estimates of 3₁ and 3₂ (at the 1% level). Interpret these coefficient estimates. (2.3) We next estimate the following model (you may assume all of the OLS assumptions hold): Iprice = Bo+B₁crime; + ß₂lnox; + ßäldist; + ßrooms; + Brooms? + Bestratio; + u OLS results (robust standard errors in parentheses): Iprice 13.656 -0.016-crime - 0.839- Inox -0.164 ldist - 0.676-rooms + (0.786) (0.003) (0.123) (0.053) (0.244) 0.072 rooms² -0.036-stratio (0.019) (0.005) n = 506, R² = 0.681, R² = 0.677 Does the coefficient estimate on rooms have the expected sign?

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P