Data Analysis of Lodging in Australia Assignment

Running head: DATA ANALYSIS OF LODGING IN AUSTRALIA
Data Analysis of Lodging in Australia
Name of the Student:
Name of the Student’s family:
Student ID:
Name of the University:
Author Note:

DATA ANALYSIS OF LODGING IN AUSTRALIA 1
Executive Summary
Students from many countries come to various cities of Australia for mainly study purpose. Australian
students also travel from their locality to other cities for same reason. Therefore, they need to hire lodging
in houses, flats or apartments for dwelling. It is true that students try to optimize their weekly rent and
bond amount and get a luxurious dwelling place. The report concentrates on the dwelling aspects and
approaches of international and Australian students and few factors that are correlated with that.

DATA ANALYSIS OF LODGING IN AUSTRALIA 2
Table of Contents
Introduction:-...............................................................................................................................................3
Section 1: Introduction to the datasets:-.......................................................................................................4
Section 2: Weekly Rent of International Students:-.....................................................................................4
Section 3: Dwelling Type Rental Bond Board Data:-..................................................................................9
Section 4: Weekly Rent of Rental Bond Board Property Data:-................................................................12
Section 5: Bond Amount Analysis:-..........................................................................................................14
Section 6: Conclusion:-.............................................................................................................................19
Conclusion of the report:-..........................................................................................................................20
References:................................................................................................................................................21

DATA ANALYSIS OF LODGING IN AUSTRALIA 3
Introduction:-
The Australian colleges, universities give chances to Australian and international students to
complete their studies (Behnke, Seo and Miller, 2015). Students come to different cities for studying and
lodge in flat, houses or apartments. Australian education is now days publicly demarcated in student’s
interest (Bryne and Hall, 2013). House-owners find a profitable business in renting their flats and houses
(Tigney and Garwood, 2015). Handicapped students sometimes search the availability of lodging
according to their adversities (Leppo, Cowthon and Bond, 2013). In that case, increment of cost becomes
the major issue of those students.
The report shows the contrast between weekly rent and amount of bond paid by the international
and Australian students. Students keep balance between these two factors keeping the factors like number
of bedrooms, suburb, dwelling type in mind (Martin, 2013).
The report elaborately describes different correlation effect between the factors, distributions of
their standard of living, many calculations and graphs. The two datasets separately indicates the
preferences of dwelling types in different suburb. The report also interprets the comparison between two
datasets.

DATA ANALYSIS OF LODGING IN AUSTRALIA 4
Section 1: Introduction to the datasets:-
Answer (a):
This assignment provides the data of Australian students who boards in the rent for various
purposes. The dataset mainly provides the factors such as amount of bonding, weekly rent of the dwelling
place, dwelling type, number of bedrooms and suburb. We would like to find out and analyze the
different aspects and interrelationship of dwelling type, weekly rent and suburb preference. We also can
draw conclusions from the analyzed data and corresponding graphs, calculations.
This assignment also invites us to collect some data of international students who boards in
Australia. In addition, we have to analyze the data numerically and graphically. The report also elaborates
the findings and comparisons between these two datasets.
Answer (b):
The international students’ data who are boarding in rent in Australia is a secondary data (Irwin,
2013). The data was primarily collected by any organization or data-collector. Then we collected the data
from them via internet and analyzed them. It involves the data of 100 students. So, the data is taken at
random and might not be biased.
The data involves total nine columns. Among them, bond amount, weekly rent, number of
bedrooms, Postcode and Length of Tenancy (in month) is numeric factor. Suburb, bond lodgment date,
bond activation date and dwelling type are attributing in nature. Here, we note that, factors like suburb,
bond amount, weekly rant, number of bedrooms and dwelling type are present in both the data.
Answer (c)
The Australian students’ data has six factors. Bond amount (in dollar), Weekly rent (in dollar),
number of bedrooms and postcode is numerical data whereas dwelling type and suburb is attribute type
data. The dataset has the collection of boarding profiles of 500 students.
The data is also secondary. The data is primarily collected by anyone other than the student and
the student receives the data as an assignment from the professor to analyze. This secondary data is the
backbone of the report structure. First five cases of our dataset is displayed in the following:
BondAmo
unt
WeeklyRe
nt
DwellingT
ype
NumberBedro
oms
Postco
de Suburb
$1,840 $460 Flat 2 2150
PARRAMAT
TA
$2,380 $595 Flat 2 2031 RANDWICK
$2,880 $720 House 4 2144 AUBURN
$3,600 $900 Flat 2 2000 SYDNEY
$2,160 $540 House 3 2144 AUBURN
Section 2: Weekly Rent of International Students:-
Answer (a):

DATA ANALYSIS OF LODGING IN AUSTRALIA 5
Figure 1: The box plot shows the scatterings of Suburbs of the students.
> summary (DwellingType3)
FLAT OR UNIT OR APARTMENT OTHERS
41 1
SEPARATE HOUSE TERRACE OR TOWNHOUSE OR SEMI-DETACHED
49 4
UNSPECIFIED
4
The summary shows that dwelling type of only 41 of international students is flat. Most of the
students (49+4=53) students live in the house.
> summary (lm(WeeklyRent3~BondAmount3))
Min 1Q Median 3Q Max
-923.63 -247.67 -41.74 203.65 1349.64
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12207.0037 1974.1351 6.183 1.49e-08 ***
BondAmount3 -1.5537 0.2746 -5.659 1.55e-07 ***
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