Build a model to predict what a house should sell for

Reference no: EM133386607

Question: You will be using the Real Estate data set (first attached )to build a model to predict what a house should sell for. This model will be used by a real estate agency to help their clients understand what their house should sell for so they can make an educated decision about listing price. Secondarily, the model will be used by a home contractor. S/he would like to be able to tell clients the selling value of adding an additional bathroom.

Briefly recap the dummy coding and missing value decisions you made in Part 1.
Prepare a professionally formatted correlation table in a separate tab/worksheet.
What is multicollinearity? Do you need to address it? If so, how?
Discuss which variables have the best correlation with price
Run a regression and discuss the results
Is the model significant?
How much of rice is explained by the independent variables?
What is the model?
Are all of the independent variables significant? Discuss.
What factors have the largest impact on home selling price?
How much does a bathroom add to the value of a home?
Run another regression (change some independent variables or change the sample of data) and discuss the results
You have been provided with the listing info and selling price on 2 houses (3rd attached) that were not in your original sample. Please use both models to predict the selling price for these 2 homes.
How accurate is your model? Please calculate your accuracy percentage as (predicted price - actual price)/actual price
Which model is better? Why?
If you had the time, money, expertise, etc. what would you have done differently and why?

Reference no: EM133386607

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