Using a sample of 546 observations, a researcher is interested in finding factors that influence house prices (measured in ten thousands). The researcher run regression of Hedonic price model that explain house prices using lot size, bed rooms, bath rooms, stories all are measured in number of units and dummy variables 2whether the house has air-conditioning, drive way, recreation room, glass show , full basement, garage place and preferred area the results are shown below. use the attached images for questions and... a. the Brush-Pegan test of hetroskedasticity is reported the below :Breusch-Pagan / Cook-Weisberg test for heteroskedasticityHo: Constant varianceVariables: fitted values of pricechi2 (1)95.15Prob > chi20.0000i.What is conclusion?yourii.What solutions do you suggest?b. Write one equation for auxiliary regression for testing multi-collinearity? State the condition for the existence of multi-collinearity?c. Assuming there is no risk of hetroskedasticity answer the followingquestionsi.Interpret R2ii.Are all the variables jointly different from zero? are shown below.reg price lotsize bedrooms bathrms airco driveway recroomfullbase gashw garagepl prefarea storiesSourceSSdfMSNumber of obs546F (11, 534)99.97Model2.6158e+11112.3780e+10Prob > F0.0000Residual1.2703e+11534237874666R-squared0.6731Adj R-squared0.6664%3DTotal3.8860e+11545713032635Root MSE15423priceСоef.Std. Err.tP> |t|[95% Conf. Interval]lotsize3.546303350310.120.0002.8581684.234438bedrooms1832.00310471.750.081-224.74093888.748bathrms14335.561489.9219.620.00011408.7317262.38airco12632.891555.0218.120.0009578.18215687.6driveway6687.7792045.2463.270.0012670.06510705.49recroom4511.2841899.9582.370.018778.97598243.592fullbase5452.3861588.0243.430.0012332.8458571.926gashw12831.413217.5973.990.0006510.70619152.11garagepl4244.829840.54425.050.0002593.655896.008prefarea9369.5131669.0915.610.0006090.72412648.3stories6556.946925.28997.090.0004739.2918374.6cons- 4038.353409.471-1.180.237-10735.972659.271I| ||

Heteroscedasticity states the condition where variance of residual term are not equal over the time…

Using a sample of 546 observations, a researcher is interested in finding factors that influence house prices (measured in ten thousands). The researcher run regression of Hedonic price model that explain house prices using lot size, bed rooms, bath rooms, stories all are measured in number of units and dummy variables 2 whether the house has air-conditioning, drive way, recreation room, glass show , full basement, garage place and preferred area the results are shown below. use the attached images for questions and...

Using a sample of 546 observations, a researcher is interested in finding factors that influence house prices (measured in ten thousands). The researcher run regression of Hedonic price model that explain house prices using lot size, bed rooms, bath rooms, stories all are measured in number of units and dummy variables 2 whether the house has air-conditioning, drive way, recreation room, glass show , full basement, garage place and preferred area the results are shown below. use the attached images for questions and...

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 2E

See similar textbooks

Similar questions

Describe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?
IV. 得分 What information can be obtained from this summary output? a to enter = 0.05, a to remove = 0.05 Analysis of Variance Source DF Adi sS Adi MS F-Value P-Value www Regression 4 37260200 9315050 45. 23 0. 000 0. 000 0. 000 Poten 1 4727687 4727687 22. 95 AdvExp 4630364 4630364 22. 48 Share 1 3009401 3009401 14. 61 0.001 Accounts 1 2129972 2129972 10. 34 0.004 Error 20 4119349 205967 Total 24 41379549 R-sq R-sq (adi) R-sq (pred) 453. 836 90. 04% 88. 05% 85. 97% Coefficients Term Coef SE Coef T-Value P-Value VIF Constant -1442 424 -3. 40 0,003 Poten 0. 03822 0. 00798 4. 79 0. 000 1. 83 AdvExp 0. 1750 0. 0369 4. 74 0. 000 1. 15 Share 190. 1 49. 7 3. 82 0.001 1.74 Accounts 9. 21 2. 87 3. 22 0. 004 1. 99 Fits and Diagnostics for Unusual Observations Std Obs Sales Fit Resid Resid www 10 4876 3942 934 2. 14 R
"In the regression model InY=b0+b1*InX+u, the coefficient b1 is interpreted as" O the intercept O A covariance O A regressor O An elasticity
1.5 Version B. Consider a panel data regression model Yit = Bo + B1 Xit + a; + eit where the unobserved heterogeneity a; is a fixed effect, and the idiosyncratic error eit is white noise. Is the random effect estimator of B1 consistent? Explain briefly.
8. Which of the following best describes the linear probability model? The model is the application of the linear multiple regression model to a binary dependent variable The model is an example of probit estimation The model is another form of logit estimation The model is the application of the multiple regression model with a binary variable as at least one of the regressors OO
In the Managerial Solution, we estimated a focus group's demand curve for iTunes downloads. The estimated coefficient on price was-413, and the 1-statistic was -12.8. nage d coe ndard Using these values, what is the standard error of this estimated coefficient? est The standard error of the price coefficient is (Enter your response rounded to two decimal places) Suppose we had another focus group sample, ran a regression on that sample, and obtained the same coefficient on price but with a standard error five times as large What can you say about the statistical significance of the price coefficient in this second sample? rt A The price coefficient would be statistically significantly different than zero at the 0.05 confidence level Sulag 50,00 t-Val Stan would be would not be
The Results below show the output of the following model: ?=?0+?1?1+?2?2+? Coefficient St. Error t-ratio Intercept 10.492 0.6655 15.77 ?1 0.0154 0.1889 0.08 ?2 0.1353 0.1889 0.72 Observations 100 ?2 0.985 Correlation matrix: X1 X2 X1 1 X2 0.950 1 Instructions: a. The above results show that the model has the problem of multicollinearity, what are the indicators of multicollinearity that can be identified from these results? b. What are the solutions to rectify multicollinearity?
Y 70 12 50 9 57 60 14 43 9 52 11 i. Find the estimators for Bi and B2 correct to decimal points and fit the regression equation for X and Y when X is the explanatory variable. Interpret the results from the obtained equation. calculate the sum of error squared. Find the variance of the sum square error ii. iii. iv. Find the standard error for B2 Find the coefficient of correlation and give its interpretation V. vi.
Consider the regression model Yi=bot Bi Xitui Suppose that you know Bo = 0. Derive the formula for the least squares estimator of B₁. The least squares objective function is O A. n O B. O C. O D. E (Yi-bo-b1Xi) i=1 n Σ (Yi-bo-biXi) i=1 2 n Σ (v₁²-bo-b₁x₁²) i=1 n E (Yi-bo-b+Xi) 3 i=1
Comment on why there exists a trade-off between variance and bias of of OLS estimators when considering the choice of including a particular explanatory variable in your underlying model. Please keep your answer precise.
1.5 Version B. Consider a panel data regression model Yit = Bo + B1Xit + a; + eit where the unobserved heterogeneity a; is a fixed effect, and the idiosyncratic error et is white noise. Is the random effect estimator of B1 consistent? Explain briefly.
1. Let kids denote the number of children ever born to a woman, and let educ denote years of education for the woman. A simple model relating fertility to years of education is kids = 0 + 1educ + u, where u is the unobserved error. (a) What kinds of factors are contained in u? Are these likely to be correlated with level of education? (b) Will a simple regression analysis uncover the ceteris paribus eect of education on fertility? Explain.