Statistical Modelling for Public Transport System in NSW

STATISTICAL MODELLING
STUDENT ID:
[Pick the date]

Section 1: Introduction
a) As the urban cities grow in size and population, an additional strain is developing on the
transport infrastructure which requires continuous investments in order to provide quick,
efficient and affordable mobility options to the dwellers. This is why there are timetable
changes and alternative overhauls in the routes of various modes of public transport so as
to cater to the highest amount of people and enhance efficiency. For determining these
changes in timetables and routes, the relevant departments tend to use the expertise of
dedicated agencies involved in research regarding public transportation system and the
behaviour and preferences of the passengers who use the same (Meyers, 2017). These
inputs are critical in order to ensure that changes brought are useful to the people at large
and ensure that maximise usage of the available infrastructure can be done so as to avoid
undue congestion at roads. Failure in this regards would lead to significant burden on the
ailing city infrastructure coupled with environmental factors involved especially climate
change.
b) A primary dataset is collected by the researcher which is not the case here as the
underlying data has been obtained by the university from a particular website. The website
has originally collected this data. Thus, the concerned data would be classified as
secondary (Eriksson and Kovalainen, 2015). The given dataset comprises of six variables
namely mode, date, tap, time, count and location.
Mode is represented using nominal scale and is a categorical variable. Date is represented
using ordinal scale since natural alignment is possible. Tap is represented using nominal
scale and is a categorical variable. Time is represented using internal scale and is a
quantitative or numerical variable. Count is represented using ratio measurement and is a
quantitative or numerical variable. Location is represented using nominal scale and is a
categorical variable (Flick, 2015). The various cases that arise in the given study
correspond to the difference in preference and travel behaviour of the individuals which
has been recorded and presented in the form of dated data.
c) The dataset 2 comprises of data which has been collected on the basis of surveying with 30
respondents. Only two variables have been reported in regards to these respondents i.e.

gender and mode of public transport. This dataset is primary as this has been collected
through the use of survey with the respondents (Hair et. al., 2015). However, this does not
imply that the accuracy of this dataset would be higher than dataset 1. This is because of the
low sample size of 30 observations coupled with use of non-probability sampling technique.
This makes the sample not being an accurate description of the underlying population
(Eriksson and Kovalainen, 2015). Gender and mode of transport both are represented using
nominal scale and are categorical variables (Hillier, 2016).
Section 2: Single variable Analysis – Dataset 1
a) The numerical summary related to the public transport usage during the given period is
highlighted below.
The above information can be graphically illustrated as highlighted below.