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I have some trouble to apply a multivariate linear regression on my data. I have two features gross_area which is continuous, nb_bathrooms which is discrete (1,2,3) and a dependent variable y which is the price. Firstly I have looked at the feature's distribution which is not normal (Skewed right). I removed some outliers. I standardized my features.I use OLS as cost function and "batch" gradient descent in order to find my parameters. I find my parameter I plot separately the gross_area against the price with parameter gross_area and the y-intercept, and a plot of the nb_bathrooms against the price with parameter nb_bathrooms and the y-intercept also.

This is what I got for the gross_area: gross_area
And for the nb_bedrooms: enter image description here

So my questions are the following, How can I fit data like nb_bedrooms since I tried polynomial terms but does not seem to fit them better.

Thank you,

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Notice that you are predicting negative prices for small gross areas. Does that make sense (Please come and live in this appartment, I will give you money if you do). I would consider using a log link function.

As for the categorical variable, I would just add indicator variables for the number of bathrooms instead of entering it as a continuous variable.

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  • $\begingroup$ Thank you, for you answer, effectively it does not make any sense. I should apply log link function on my predicted price matrix ? $\endgroup$ – Marc Lamberti Apr 8 '15 at 11:54
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    $\begingroup$ I prefer to use a link function instead of transforming the dependent variable. That way you continue to model the mean price instead of the mean log(price). $\endgroup$ – Maarten Buis Apr 8 '15 at 12:12
  • $\begingroup$ I understand, but I don't see how can I apply this actually :/ $\endgroup$ – Marc Lamberti Apr 8 '15 at 14:23
  • $\begingroup$ Here is one brief explanation, with many references, also in the comments, to check out if you want to know more. $\endgroup$ – Maarten Buis Apr 9 '15 at 7:57

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