Prepare a report in a document file (.doc or .docx) which includes all relevant tables and figures, using the following structure:
 Section 1: Introduction
 Give a brief introduction about the assignment and search related article and write a paragraph of summary which supports your assignment. You need to give the full citation of the article.
 Dataset 1: Give a short description about this dataset. Is this primary or secondary data? What are types of variablesinvolved? Explain briefly what are the possible cases used in this study.
 Dataset 2: Explain how you collect the data and discuss its limitation (e.g. whether your sample is biased). Is this primary or secondary data? What is/are the type(s) of variable(s) involved? Give a description of cases you consider for this data set.
 Section 2: Analysis of single variable in Dataset 1
 To answer research question “Which type of public transport was most used by the NSW people during 8th to 14th of August 2016?”, provide a suitable numerical summary and graphical display for the variables mode of Dataset 1. Give a detailed comment to answer the research question.
 Now to answer research question “Are there more than 50% of public transport users in NSW use the particular mode of transport found in Part a?” setup an appropriate hypotheses, perform hypotheses test and answer the research question by writing the conclusion of the test.
 Section 3: Analysis of two variables in Dataset 1 NSW Government need to decide on whether they have to build an underground
Railway line from either Parramatta, Bankstown or Gosford to central. To prepare a recommendation for this;
 Give a numerical summary and an appropriate graphical display for the variables location, by only considering those three stations; and the variable count by considering the data with trains only.
 Perform a suitable hypothesis test at a 5% level of significance to test whether there is difference between mean counts of taps on and off.
 Use the conclusion of the test in part b and the outputs in part a to write a recommendation to NSW government.
 Section 4: Collect and analysis Dataset2
You are interested in finding whether there is a difference in preference between different gender in terms of their transport mode (Bus, Train, Ferry and Light Rail). by considering appropriate number of cases and variable, give a proper graphical display and use it to write a comments
Introduction
a). This assignment is based on the analysis of New South Wales government transport by Bus, Train, Ferry and Light Rail. The assignment entails the analysis of various aspects associated with the NSW transport such as time, location, count and date of the travel.
Based on the New South Wales Long Term Transport Master Plan (December, 2012), the New South Wales public transport there are four basic means of transport offered i.e. by bus, by train, by ferry and by light Rail.
It is important that the New South Wales government is undertaking long term master plan, uses the opportunity to give clear and concise and better transport services to the people of New South Wales that is passengers over a period of time (Bowditch G 2018). This paper therefore presents some of the recommendations to the New South Wales government based on the analytical findings to enable adjustments in a particular variable on transport system. The assignment is thus a comprehensive analysis of factors such as mode of transport, gender, location, and times of travel and date of tap. The possible cases used in gathering the data includes direct interviewing of the travelers, observation and filling of the questionnaires by the actual passengers.
b). Dataset 1is a secondary form dataset since it originates from the NSW master plan. Dataset 1 contains information that is related to the New South Wales transport used by people. The dataset Based on the New South Wales Long Term Transport Master Plan (December, 2012), the New South Wales public transport there are four basic modes of transport offered i.e. by bus, by train, by ferry and by light Rail.
Dataset 1 is 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 travel (Type of public transport i.e. Bus, Train, Ferry and Light Rail)
 Date of the tap on or off held (From 8^{th}to 14^{th} August 2016)
 Time of travel
 Location
 Tap (On or off)
 Count
 Location (Locations of stops. For bus postcodes and other names of the stations.
The possible cases used in the study are observation. This is because the data was collected just on the basis of observing.
c). 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. 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 travelers 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: First, 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 45 cases or items. Second, the dataset 2 comprises of categorical variables only which cannot be subjected to more and extensive statistical analyses since only demographical aspects/ variables i.e. gender and mode of transport are presented in the dataset 2.
Dataset 2
Section 2: Analysis of Single Variable in Dataset 1
Type of transport that was commonly used.
Transport by train was the most used mode of transport in New South Wales transport system from 8^{th} to 16^{th} August 2016 (482 times).
The variable ‘mode’ is categorical data type. Because of this, a single frequency table can be used to show the numerical summary as shown below. Table 1. The Numerical Summary for the Modes of Transport used between 8^{th} and 14^{th} August 2016
Row Labels 
Count on Mode 
The Proportion (P) 

Bus 
472 
0.4720 

Ferry 
26 
0.260 

Light Rail 
20 
0.20 

Train 
482 
0.4820 

Grand Total 
1000 
1 
Figure 1. PieChart Showing the distribution of travel by people of New South Wales between 8^{th} and 14^{th} of August 2016.
Based on Pie Chart graph above, it is crystal evident that yellow color is much spread and thus represents highest number of count (48%). It can thus be asserted that most of people are using train as means of public transport. Travel by bus is the second highest (about 47%) and travel by Light train is the least mode of transport that people of New South Wales are using (2%).
Section 2 (b)
Testing the Hypotheses
The proportion people using train (p) =0.482 (Highest value chosen from proportion column) N=1000
Step 1: Stating the Hypotheses
The null and alternative hypotheses can be stated and formulated as follows;
H0: p= 0.5
H1: p>0.5
Step 2: Checking if the condition is satisfied.
Is condition satisfied?
Np0≥10 i.e., 1000 × 0.5≥10 = 500≥10
N (1p) ≥10 i.e., 1000 × (10.5) ≥ 10 = 500 ≥ 10
From the above illustration, it is true that the condition are satisfied. Hence it implies that the p value can be obtained and computed as the region under the tail of the standard normal beyond the value of
Calculation of test statistics
?=
Then we have,
Test statistics ?==== 1.140
Step 3: Comparison
P (Z>1.140)
=0.1251
The Pvalue obtained is greater than alpha (pvalue>0.05) where α=o.05
Step 5: Conclusion
The test statistics is calculated below 1.645, i.e., 1.14 hence we do not reject the null hypothesis (H0) and thus we conclude that there is no significant evidence that there are more than 50% of public transport users in New South Wales in use of a particular mode of transport obtained in section (a) above.
Section 3
Part (a)
Variables ‘location’ and ‘count’ are categorical and quantitative respectively. So suitable graphical display for one categorical and quantitative are dot plot and histogram and box plot. We have used the box plot to show the graphical representation of this two variable.
Analysis of Single Variable in Dataset 1
Numerical Summary
Box Plot
In the above boxplot, xrepresent the location of train station and yaxis represent the number of people. Box plot for paramatta station is skewed to the right which has highest of 443 people, whereas for bankstown station has highest of 125 people
Part 3 B
Variables involved:
 Tap categorical
 Count quantitative
Step1: Stating the Hypotheses
H0: all the means are equal
H1: at least two means are different
Step2: Checking the Condition
Condition check
 Sample size (n) in each group are ≥30 (satisfied)
 Standard deviation is similar in each group (yes), because none of the standard deviation is twice the other standard deviation.
All the condition are satisfied.
Step3: Testing the hypothesis
Step4
From the diagram we can get value Pvalue which is 0.243
Step5
Since Pvalue i.e., 0.243>0.05, so do not reject H0, which means there is not enough evidence for difference in the mean.
Section 4
In this survey we have got two variable and twentyfour cases. Two variables are ‘Gender’ and ‘Mode’. Both are categorical variables, so we need to use segmented segmented bar chart for graphical representation of two categorical variables.
In the above segmented bar diagram xaxis represents the variable ‘gender’ and yaxis represent the variable ‘mode’. In each bar blue color represents the portion of male and grey color represents the portion of female. Male prefer to use train more than the female. In case of ferry, female prefer to use ferry more than the male. Similarly, male like to use bus as modes of transport than that of female. Whereas female use light rail more than male.
Section 5: Discussion and Conclusion
Based on the various analysis conducted under the sections above, the following can be discussed and concluded. Also, few recommendations regarding the analysis can be made.
Travel by both train and bus is the most mode of transport used by people of New South Wales and for this reason, the government should consider making the two transport much more extensive.
In the bar diagram xaxis represents the variable ‘gender’ and yaxis represent the variable ‘mode’. In each bar blue color represents the portion of male and grey color represents the portion of female. Male prefer to use train more than the female. In case of ferry, female prefer to use ferry more than the male. Similarly, male like to use bus as modes of transport than that of female. Whereas female use light rail more than male.
In regard to the future researches, I would recommend that a larger sample population be applied in future research work as small sample of the population tend to yield produce biased information which definitely leads to wrong assertions and conclusion about particular aspect under consideration.
From the analysis in section 2, it is also evident that claim that there are more than 50% of public transport users in New South Wales using a particular mode of transport is null. All modes of transports indicates a value less than 50 %. For instance, transport by train represents 48% while travel by Light Rail represents 2%
Most of males are found to prefer use of train as a mode of transport than female. In the case of using a ferry, the female are the frequent users than males. Whereas, the female also prefer light rail than male. Consequently, a larger sample population should be applied in future research work as small sample of the population tend to yield in appropriate results for inferences
References
Bruce, P. (2014) Introductory Statistics and Analysis. 7^{th} ed. McGrawHill: Boston.
Bodwitch, G. (2018) NSW Long Term Transport Master Plan. Smart Infrastructure Facility. 18^{th} September, p. 715.
Diggie, P. and Jackton, P. (2015) Statistics. 10^{th} ed. Routledge: New York.
Jarman, K. (2015). Beyond Basic Statistics. 6^{th} ed. New Jersey: John Wiley and Sons.
Lock, R., Patti, F., Morgan, K., Erick, F. and Dennis, F. (2013). Statistics. Cambridge University Press: Cambridge.
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