Reference no: EM132162970

Questions

(a) Provide a scatter plot between the variables price (Y axis) and sqmt (X axis) and comment on your plot.

(b) Estimate the following linear regression model and interpret the estimate of β₂? Provide your regression output.
price_i = β₁ + β₂sqmt_i + e_i

(c) Does an increase in the total living area by one square meter increases the expected price of a house by more than 900 dollars? Test the hypothesis at the 1% signiftcance level.

(d) Construct a 99% conftdence interval for the coefftcient of sqmt. Interpret this interval.

(e) Estimate the following regression model using Eviews and write down the regression equation including standard errors, t-statistics, R-squared,
F-statistics and the sample size.
price_i = α₁ + α₂ sqmt + α₃ age + α₄ bdrms + ∈_i

(f) Write down the coefftcient of determination and interpret it.

(g) Interpret the estimates of α₃ and α₄. Are the signs of these two estimates consistent with your expectation? Why/Why not?

(h) Test if each coefftcient is signiftcantly different from zero. Use the 5% signiftcance level. (critical value from EXCEL =T.INV.2T(0.05,1076))

(i) Using the 5% signiftcance level test the overall signiftcance of the model. (=F.INV(0.95,3,1076)).

(j) Compute and interpret the elasticity of price with respect to sqmt for a home that is 250 square meters large, 15 years old, and has two bedrooms.

(k) At a 5% signiftcance level test for the existence of heteroskedasticity using a White test (no cross terms). State all steps in conducting a hypothesis test for heteroskedasticity and attach your Eviews output.

(l) Re-estimate your model in question (e) with white consistent standard errors and provide your output. What changes would you notice when compare with the regression output you obtained in question (e).

(m) Compare the two models you estimated in question (b) and question (e). Choose a preferred model. Explain why?

Attachment:- Assignment data.rar