Regression Analysis for House Market Value Estimation

STATISTICS
STUDENT ID:
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1) Introduction
The key aim of the given report is to frame an appropriate regression model for the variables
that have been presented. The total variable count for the given dataset is five and all these
variables are in the form of quantitative data allowing the performing of regression analysis.
As the data has been provided for each of the past 15 years, hence the sample size is 15. The
primary objective is to develop a multiple regression model where the market price would be
the dependent variable while the remaining four variables would serve as independent
variables. The measurement scale for the different variables is ratio or interval so as to
facilitate the representation of these variables in the form of a multiple regression model. The
various variables provided seem suitable for estimation of market value of house. Once the
multiple regression model is developed, then suitable changes would be made to develop a
more suitable model and to weed out the independent variables which do not have a
significant relationship with the dependent variable.
2) Scatter Plot
Between every independent variable and the underlying dependent variable, scatter plot needs
to be drawn which is carried out in this section.
The requisite scatter plot between independent variable (Sydney price index) and dependent
variable (market price) is as illustrated below.
Considering that the best fit line shown in the plot above has a positive slope, hence it can be
concluded that the underlying linear relationship between the given two variables is positive.

The deviation of the various scatter points from the line of best fit is also minimal which is
indicative of the fact the underlying magnitude of the correlation between the variables is
high. As a result, it would be fair to conclude that the given two variables (Sydney Price
Index & Market price) have a positive and strong relationship in strength (Flick, 2015).
The requisite scatter plot between independent variable (annual % change) and dependent
variable (market price) is as illustrated below.
Considering that the best fit line shown in the plot above has a positive slope, hence it can be
concluded that the underlying linear relationship between the given two variables is positive.
The deviation of the various scatter points from the line of best fit is quite large which is
indicative of the fact the underlying magnitude of the correlation between the variables is low
to moderate. As a result, it would be fair to conclude that the given two variables (Annual %
change & Market price) have a positive but weak to moderate relationship in strength
(Eriksson and Kovalainen, 2015).
The requisite scatter plot between independent variable (Age of House) and dependent
variable (market price) is as illustrated below.
.

Considering that the best fit line shown in the plot above has a negative slope, hence it can be
concluded that the underlying linear relationship between the given two variables is negative.
The deviation of the various scatter points from the line of best fit is also not very large which
is indicative of the fact the underlying magnitude of the correlation between the variables is
moderately high. As a result, it would be fair to conclude that the given two variables (Age of
house & Market price) have a negative and moderately strong relationship in strength (Hair,
et al., 2015).
The requisite scatter plot between independent variable (Area of House) and dependent
variable (market price) is as illustrated below.