Comprehensive Data Analysis of Lodging in Australia - Report

This report presents a comprehensive data analysis of lodging in Australia, focusing on the dwelling aspects and approaches of international and Australian students. The analysis explores the correlation between weekly rent, bond amounts, dwelling types, and suburb preferences. The report examines two datasets, one for international students and another for Australian students, revealing key insights into their living arrangements. It includes statistical summaries, graphical representations, and comparative analyses to highlight trends and patterns. The findings provide valuable information regarding the factors influencing student lodging choices, such as the number of bedrooms, location, and type of dwelling. The report concludes with recommendations for students seeking optimal lodging options based on their preferences and financial considerations.

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:
BondAmoun
t WeeklyRent
DwellingTyp
e
NumberBedroom
s
Postcod
e Suburb
$1,840 $460 Flat 2 2150 PARRAMATTA
$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 ***
---

DATA ANALYSIS OF LODGING IN AUSTRALIA 6
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 398.2 on 97 degrees of freedom
Multiple R-squared: 0.2482, Adjusted R-squared: 0.2404
F-statistic: 32.02 on 1 and 97 DF, p-value: 1.546e-07
> cor(WeeklyRent3,BondAmount3)
[1] -0.4981778
The value of multiple R2 in linear regression model and correlation coefficient between weekly
rent and Bond Amount shows that in this dataset both of these factors are neither linearly related nor
highly correlated.
5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0
distribution of weekly rent
amount of weeklyrent
Figure 2: The box plot shows the distribution of amount of weekly rent of the students.
The median of the weekly rent is 1000 and it involved few outliers too.

DATA ANALYSIS OF LODGING IN AUSTRALIA 7
7000 7100 7200 7300 7400
distribution of amount of bond
amount of bond
Figure 3: The box plot shows the distribution of amount of bond of the students.
The median of the bond is 7250 and it involved no outliers.
Answer (b)
> range(WeeklyRent3)
[1] 82.5 650.0
> range(BondAmount3)
[1] 330 2600
The ranges of Weekly rent and Bond Amount show the widely spread of these two factors. From,
the previous graphs, we observed that weekly rent contains outliers whereas amount of bond pay does not
contain outliers.

DATA ANALYSIS OF LODGING IN AUSTRALIA 8
Histogram of WeeklyRent3
WeeklyRent3
F r e q u e n c y
0 100 200 300 400 500 600 700
0 1 0 2 0 3 0 4 0
Figure 4: Histogram of Weekly Rent of the international students.
Histogram of BondAmount3
BondAmount3
F r e q u e n c y
0 500 1000 1500 2000 2500 3000
0 1 0 2 0 3 0 4 0
Figure 5: Histogram of Bond Amount of the international students.

DATA ANALYSIS OF LODGING IN AUSTRALIA 9
Both the histogram plots of Weekly rent pay and bond amount shows that their distributions are
positively skewed. Their distribution does not follow normal distribution perfectly. An interesting point is
that most of the international students pay weekly rent of $200 to $300 and most of the students pays the
bond amount of $500 to $1000.
> summary(bedroom1)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 2.000 2.000 2.404 3.000 5.000
> summary(bedroom2)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 1.000 2.000 1.748 2.000 4.000
The summary measures of number of bedrooms indicate that the international students have
greater number of bedrooms than Australian students have (in comparison with mean). It shows that
international students are more luxurious than Australian students.
Section 3: Dwelling Type Rental Bond Board Data:-
Answer (a)
Numerical Summary of Australian students’ Dwelling Type:
> summary(DwellingType)
Flat House
479 21

DATA ANALYSIS OF LODGING IN AUSTRALIA 10
0
100
200
300
400
500
Flat House
DwellingType
fre q u e n c y
Showing Dwelling Type of the students
Figure 6: A Bar chart to show dwelling types of the students.
Answer (b)
1-sample proportions test without continuity correction
/////output///////
data: rbind(.Table), null probability 0.5
X-squared = 419.53, df = 1, p-value < 2.2e-16
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.9366484 0.9723677
sample estimates:
p
0.958
The information about dwelling types of the students is attribute type data. Definitely, the data
informs that students having dwelling type “House” is 21 in number. Now, we apply test of significance

DATA ANALYSIS OF LODGING IN AUSTRALIA 11
of proportion of house rent and flat rent type by 95%. According to the estimated sample of Dwelling
Type, the p-value interprets that null hypothesis is not rejected.
Therefore, we can conclude that, rent type of house has proportion of less than 10% according to
the values of 95% confidence interval.
Answer(c)
Numerical Summary of Suburb of the students:
> summary(Suburb)
AUBURN PARRAMATTA RANDWICK SYDNEY
57 148 118 177
Flat
House
AUBURN PARRAMATTA RANDWICK SYDNEY
Suburb
D w e llin g T y p e
Relation of Suburb and dwelling type of the students
Figure 7: A comparative scatter plot between Suburb and dwelling type of the students.
The graph shows that the main Suburb is distributed in four parts such as Auburn, Parramatta,
Randwick and Sydney. Similarly, dwelling types is mainly divided in two parts such as House and Flat.
The people whose suburb is Sydney do not live in house. House dwellers are maximum in number in case
of Auburn. The maximum number of student has suburb in Sydney and minimum number of student has