Empirical Business Analysis: Regression Model for Selling Price of a House

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Empirical Business Analysis
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Empirical Business Analysis
Executive Summary
The purpose of the paper was to develop regression model that can be used
to describe how various factors impact the selling price of a house and
determine whether inclusion of interaction variable would improve the
quality of the model. Two models have been created; one without the
inclusion variable and the other with inclusion variable. A comparison of the
two have showed that there is no justification that inclusion of the interaction
variables would improve the quality of the model. Therefore, the initial model
without the interactive variables has been considered as the best model to
explain the relationship between the variables in this case.
Introduction
Regression analysis is an important aspect in statistics that is used to
determine the relationship that exist between variables. The purpose of this
paper is to develop a regression analysis model that can be used to explain
how the selling price of a house is affected by factors such as land area,
equivalent area, number of rooms, the condition of the house and the
number of years the house has been in existence. An initial model that treats
the selling price as the dependent variable and all the other variables as
independent has been created. Another model that includes interaction
model is created and compared with the initial model to determine whether
the quality of the regression model would be improved or not. The result

indicate that the initial model is the best model and inclusion of interaction
variables does not improve the quality of the regression model. However,
these findings might have adversely affected by the huge number of
independent variables or errors that emerged during data collection.
Regression Model
A regression model is to be created to determine to determine how the sales
price of a house is affected by factors such as land area, the number of
rooms, the equivalent area, the condition of the house and the number of
years since the house was built. In the first scenario, the dependent variable
is the sales price while all the other variables are treated as the independent
variables. The multiple regression model created is as shown below: