Analysis of New South Wales Public Transportation System

Running head: STATISTICS 1
1) Introduction:
a) The main objective of the assignment is to test skill in examining the data from the dataset
provided by our lecturer and the data by me. This assignment is all about using statistical
modelling techniques learned through the trimester to develop our knowledge on solving
particular business problems. In this report various aspects of New South Wales government
public transportation system have been evaluated after applying relevant statistical theories
and concepts. It doesn’t only involve hypothetical tests but also check the conditions to
validate its conclusion. NSW government provides various modes of transportation including
bus, train, light rail, ferries etc. We have been allocated data base on the same obtained
from New South Wales official site to analyse various factors. Public transport is the most
significant services to be provided by the NSW government for the smooth and effective
communication of people (Ben Barnes, 2013). However, every government have to be
careful enough to improve the services quality even better. To provide the better services,
efficient revenue generation is important. In this assignment we are going to focus on New
South Wales transportation system to solve specific business problem including analysis of
opal tap on and tap off, total usage of public transportation, whether New South Wales
government think of developing underground subway between train stations, which mode
of transportation generates the most revenue for the New South Wales government.
b) Data set 1 is not an original data because it is the subset of sample data file from New South
Wales transport. The dataset 1 is considered as a secondary data because it is extracted
from the original data for the research purpose. Dataset 1is a secondary form dataset since
it originates from the New South Wales master plan. Dataset 1 contains information that is
related to the New South Wales transport preferred by people of New South Wales. The
dataset is based on the New South Wales Long Term Transport Master Plan of December,
2012. According to the dataset, the New South Wales public transport is made up of four
basic modes of transport such as by bus, by train, by ferry and by light rail. The so presented
dataset also comprises of these variables (mode of transport, date, and tap, time of travel,
location and count). The date of transportation is available on the dataset as it gives the day
date of the travel by the people of New South Wales. The date presented is between 8th to
14th of August 2016. The variable “times” is as well indicated in the data. Time as a variable
will be important in the analysis since it enables travellers to plan for their journey.
Furthermore, dataset 1 is comprises of 1000 samples. The data Dataset 1 is thus a secondary
dataset since it includes information collected by the government and the data was initially
collected for other related research work. The dataset contains variables such as mode of
transport, gender, time of the tap (on or off), location and count. The possible cases applied
in the study are observation and interviews. This is because the actual participants were
involved in the survey and it therefore implies that they were either subjected to interviews
or were given some questionnaires to fill. Observation was the key research case that was
possibly applied since the survey required much attention in getting and recording to some
considerable and important aspects.
c) Dataset 2: Dataset is a dataset that comprises of only two variables i.e. mode of
transportation and the gender. Gender in this dataset represents the demographic aspect.

STATISTICS 2
The modes of transport in dataset 2 are four. They include transport by bus, by ferry, by rain
and by light train. The dataset 2 has comprise of a sample of 25 from which 14 are females
while 11 are male. Dataset is a primary dataset as the data was collected from the actual
traveller from the New South Wales. The possible cases applied in collecting dataset 2 was
through observation. The researcher (in this case “me”) conducted actual study by observing
the factors under consideration and recording. However, on critical examination of dataset
2, dataset 2 is biased due to the following reasons;
i. The total sample presented in the dataset is comprises of only 25 cases which is
relatively smaller and thus could not be used up in the analysis. The minimum
sample should be 3o cases/ items.
ii. The dataset only comprised of categorical variables which cannot be subjected to
more statistical analysis since only demographical aspects/ variables i.e. gender and
mode of transport is presented in the dataset.
Section 2(a)
Variable mode is one categorical data. As it is one categorical variable we can use only one numerical
summary.
Numerical summary
So with the table of numerical summary, it is evident that bus has the highest proportion of 0.483.
Graphical Summary
Pie chart
Row Labels Count of mode proportion
Bus 483 0.483
Ferry 38 0.038
light rail 16 0.016
Train 463 0.463
Grand Total 1000 1

STATISTICS 3
48%
4%
2%
46%
Total NSW people using public transport
during 8th to 14th august,2016.
bus
ferry
lightrail
train
From the above pie chart it is clearer that highest number of the people which is 48% of NSW is
using bus to transport. After that 2nd highest number of people prefer train to travel with the
percentage of 46. Ferry and light rail has the least which is 4% and 2% of people who prefer to travel
by ferry and bus.
Section 2b:
To answer the hypothesis, we have to follow 5 steps as given below:
Step 1. Stating the hypotheses
H0: p=0.5
H1: p>0.5
Step 2. Checking if condition is satisfied
Is condition satisfied?
np0≥10 = (1000*0.5) =500≥10
n (1-p)≥10=1000(1-0.5) =500≥10
As 500 is greater than 10, thus the conditions have been satisfied. Therefore, p-value can be
computed as the area in tail(s) of a standard normal beyond z.
Step 3: Computing the test statistics
Statistic test