estimates of the beta values 2.11 The file br2.datcontainsdataon 1080 houses sold in Baton K mid-2005. include price, the house site in square feet, its

estimates of the beta values <.i1 the file br2.dar contains data on houses sold in baton rouge louisiana durin mid-2005. include sale price house size square feet its age whether has a pool or fireplace is waterfront. also included an indicator variable traditional indicating style not. descriptions are br2.def. plot against for with style. traditional-style estimate linear regression model b2soft e. interpret estimates. draw sketch of fitted line. quadratic compute marginal effect additional foot living area home space. elasticity respect to soft graph line that tangent curve house. son:10 regressions and least squares residuals them sqft. do any our assumptions appear violated adio one basis choosing between these two specifications how well fit by model. compare sum squared from models which lower sse does having indicate log-linear v1 area.>

2.11 The file br2.datcontainsdataon 1080 houses sold in Baton K mid-2005. include price, the house site in square feet, its age, has a or fireplace or iS on the waterfront. Also included is an indicator TRADITIONAL indicating whether the house style is traditional or not.8 variable a) Plot house price against house size for houses with traditional style. (b) traditional-style houses estimate the linear regression model PRICE l + 2SQFT e. Interpret the estimates. Draw a sketch of the fitted line. (c) the traditional-style houses estimate the quadratic regression model PRICE = at + a:SQFT2 + e. Compute the marginal effect Of an additional square foot of living area in a home with 2000 square feet of living space. Compute the elasticity of PRICE with respect to SQFT for a home with 20m square feet of living space. Graph the fitted line. On the graph, sketch the line that is tangent to the curve for a 2000-square-foot house. (d) For the regressions in (b) and (c) compute the least squares residuals and plot them against SQFT. Do any of our assumptions appear violated? (e) One basis for choosing between these two specifications is how well the data are fit by the model, Compare the sum of squared residuals (SSE) from the models in (b) and (c). Which model has a lower SSE? How does having a lower SSE indicate a "better-fitting" model? (f) For the traditional-style houses estimate the log-linear regression model In(PRlCE) = + .12SQFT + e. Interpret the estimates. Graph the fitted line and sketch the tangent line to the curve for a house with 2000 square feet Of