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Use the housing price data in HPRICEl for this exercise.(i) Estimate the model$\log ($price$)=\beta_{0}+\beta_{1} \log ($lotsize$)+\beta_{2} \log (s q r f t)+\beta_{3} b d r m s+u$and report the results in the usual OL.S format.(ii) Find the predicted value of log(price), when lotsize $=20,000, s q r f t=2,500,$ and $b d r m s=4$ Using the methods in Section $6-4,$ find the predicted value of price at the same values of the explanatory variables.(iii) For explaining variation in price, decide whether you prefer the model from part (i) or the modelprice $=\beta_{0}+\beta_{1}$ lotsize $+\beta_{2} s q r f t+\beta_{3} b d r m s+u$

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(i) (ii) see video (iii) model from part (i) better

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Chapter 6

Multiple Regression Analysis: Further Issues

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Kun H.

October 10, 2022

the OLS interpcet is not 5.67.? i got -1.297

Arken Y.

November 8, 2022

the video doesn't seem to be working

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not gone. This is the result from estimating the lock lock model. We have the log price as a function of intercept. Um log of lots sized lock of square feet and a number of bedrooms in level. There are 88 observations and Our Square is .634. The adjusted our spare is 0.6 30 Hard to. We would predict the price of an estate with ailes not size of 20,000 Square feet of 2500. And it has four bedrooms. We will pluck these values into the estimated equation in part one and we get the fitted lock, prize lock price value as 12 point nine. You convert your price. The price levels are not just their exponential of this value. We need to multiply it with a term 1.023. And this term is there intercept? Oh not the intercept, it's a slope The Beta one estimate Yes. On a quantity am I had, which is defined as the exponential of lot of price. The fitted value From the Regression Equation in Part one. So the first step to get this value is to find the fitted value lock price had from equation Estimated in Part one. Then second thing is to you regress blogger priced on the variable M I had we defined as the exponential of the fitted values. After that we can get the slope coefficient which is this, we multiply this with the exponential of 12.9 and women get 400 and 9000 519 dollar. That is the price of a house. With these characteristics R. Three. I need to correct this. So when we run a regression with all variables in levels the r square is coin 672 When we run another regression where we have price depends on the locker price and the longer price is the only independent variable. We get our square of point seven 38 slightly higher. Yeah, So the model with Ollie Locke Price as the independent variable, It's the data better, slightly better.

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