LSP = 01 + 02 SFLA + 03 BEDS + 04 BATHS + 05 STORIES + 06 VACANT + 07 AGE + u, (1) where u is an error term. Note that the sub-index i of each variable has been suppressed in the above equation. LSP = ln(Selling Price), which is a function of: SFLA - Size of Living Area (in square feet) BEDS - Number of Bedrooms BATHS - Number of Bathrooms STORIES - Number of Stories VACANT - Vacancy Status (1 if vacant, 0 if not at the time of the sale) AGE - Age of the House in Years

Write the model (1) using a vector form in a sample version as follows:

y i = 01 x i1 + 02 x i2 + + 07 x i7 + u i = x 0 i 0 + u i , (2) where i = 1,...,N, 0 = ( 01 ,..., 07 ) 0 , x i = (x i1 ,...,x i7 ) 0 = (1,SFLA,...,AGE) 0 , and the definition of y i should be obvious. Further define X = (x 1 ,...,x N ) 0 in case one may need this notation to answer the following questions. (a). To obtain the OLS estimate of 0 of (2), we need to minimize an objective function. Please write down the correct function form of the objective function. (1 point) (b). Describe the basic assumptions of the classic linear regression models using the nota- tions of (2). (2 points) (c). Provided that these assumptions hold, what conclusions can you make about the OLS estimators?