Foundation Skills in Data Analysis

FOUNDATION SKILLS IN DATA ANALYSIS
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Introduction
Statistical analysis is a useful tool which can either be used to summarise and find
relationship between the given variables or estimate population parameter. For the given task,
the focus is on descriptive statistics and hence prediction about population parameters is not
required. The objective of the given report is to use the correlation and regression analysis for
carrying out the sample data analysis to estimate the values of the dependent variables and
estimation of the underlying nature of relationship between selected variables. Also, time
series analysis has been carried out for median prices in Melbourne based on the sample data
provided.
Relationships
(a) The objective is to use the given sample data to highlight if there does exist any
relationship between house price and suburb. In order to ensure the same, the contingency
table has been drawn with regards to house prices for the three suburbs. This is highlighted
below.
On the basis of the above, it is apparent that house prices seem to be cheapest in Suburb 1 and
most expensive in Suburb 3. In case of suburb 1, there are 6 houses in the price range of $
224,000 - $ 423,000 which there is no property in suburb 3 in this price bracket. For the
initial four price classes, the highest representation of houses is from suburb 1. On the
contrary, in the highest price bracket of $ 1,624,000 - $ 1,823,000, there is no property from
suburb 1 or suburb 2 and only 4 houses from suburb 3 have prices in this interval. In the
second highest price class, the highest representation is from suburb 3. Thus, it can be
concluded that there is significant relationship between suburbs and house price since the
house prices across the suburbs tend to significantly differ.

(b) The objective is to highlight based on the sample data if the underlying relationship
between each of the independent variables ( i.e. LandSizeSqm, HouseAreaSqm,
WeeklyRent ) and the dependent variable i.e. HousePrice is linear or non-linear. The
appropriate tool to analyse the same would be scatter plot between the independent and
dependent variable chosen.
The relevant scatterplot between LandSizeSqm as the independent variable and HousePrice
as the dependent variable is shown below.
Based on the above scatter plot, it is apparent that there is a significant deviation of the scatter
points from the line of best fit. Also, the pattern of the scatter point seems to resemble a circle
and do not fit in a linear pattern. As a result, it would be fair to conclude that the relationship
between the given variables would be non-linear (Eriksson and Kovalainen, 2015).
The relevant scatterplot between HouseAreaSqm as the independent variable and HousePrice
as the dependent variable is shown below.

Based on the above scatter plot, it is apparent that there is not significant deviation of the
scatter points from the line of best fit. Further, the coefficient of correlation for the two
variables tends to exceed 0.5 which implies that strong linear relationship is present between
the two variables. As a result, it would be appropriate to conclude that the underlying
relationship between the given variables is linear (Flick, 2015).
The relevant scatterplot between WeeklyRent as the independent variable and HousePrice as
the dependent variable is shown below.