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# Maths - Simple linear regression Problem

Question # 00012462
Subject: Mathematics
Due on: 05/12/2014
Posted On: 04/16/2014 02:53 AM

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1. In this exercise we will be building regression models for predicting house prices. We will be using data collected on 91 houses in Gainesville, Florida. The dataset contains the selling price of each house and information on four other explanatory variables, and it can be found on moodle.

The variables contained in the dataset are:

Y: Price. It is measured in thousands of dollars.

X1: Area. It is the floor area of the house measured in thousands of square feet.
X2: Bed. The number of bedrooms of the house.
X3: Bath. The number of bathrooms of the house.
X4: Pool. Indicates whether the house has a swimming pool. (it takes the value 1 if the house has a pool, and 0 otherwise).

Questions:
Simple linear regression.

i) Fit 3 simple linear regression models with area, bed, and bath as the only predictor in each. Report the estimated parameters from the model that you consider to be the most useful in predicting house prices, along with an explanation why you consider that model to be the most useful one.

ii) Assuming that the best single predictor model is area, provide a 99% confidence interval for the mean price for a house area = 2500 square feet.

iii) Assume your neighbors own a house with area = 2500 square feet. Obtain a 99% prediction interval for the selling price of the house if they decided to sell it.

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#### Maths - Simple linear regression Problem

Tutorial # 00012014
Posted On: 04/16/2014 02:54 AM
Posted By:
vikas
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Maths_-_Simple_Linear_Regression_Solution.docx (19.18 KB)
Preview: predicting xxxxx prices xx will be xxxxx data collected xx 91 xxxxxx xx Gainesville, xxxxxxx The dataset xxxxxxxx the selling xxxxx of xxxx xxxxx and xxxxxxxxxxx on four xxxxx explanatory variables, xxx it xxx xx found xx moodle The xxxxxxxxx contained in xxx dataset xxxxxx xxxxx It xx measured in xxxxxxxxx of dollars xxx Area xx xx the xxxxx area of xxx house measured xx thousands xx xxxxxx feet xxx Bed The xxxxxx of bedrooms xx the xxxxx xxx Bath xxx number of xxxxxxxxx of the xxxxx X4: xxxx .....
MATHS-House_Data_Solution.xls (51.5 KB)
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