Mathematical and Statistical Methods

Prepared by: Dr Anies Hannawati Moderated by: Prof Paul Kwan March, 2023

Assessment Details and Submission Guidelines
Unit Code	MDA511
Unit Title	Mathematical and Statistical Methods
Term, Year	T1, 2023
Assessment Type	Assignment 2, Group
Assessment Title	Australian Population Analysis and Prediction
Purpose of the assessment (with ULO Mapping)	This assignment assesses the following Unit Learning Outcomes; students shouldbe able to demonstrate their achievements in them. c. Compare solutions to problems using appropriate statistical tools. d. Analyse and interpret results from descriptive and predictive data analysis.
Weight	25% of the total assessment
Total Marks	100; Final Marks = Total Marks x SPARKPLUS RPF Factor
Word limit	Minimum 1000 words
Due Date	Demonstration/Viva, Week 11, Monday, 29th May 2023, 02:30PM Week 11, Monday, 29th May 2023, 23:59 PM
Submission Guidelines	• All work must be submitted on Moodle by the due date. • Students are required to submit three files: a document file, the presentation PowerPoint file, and a Python Jupyter Notebook or an Excel Workbook. • SPARKPLUS Self & Peer Assessment and Feedback for group assignment is a compulsory task for each student. The SPARKPLUS results will determine the final assignment mark. • The assignment must be in MS Word format, 1.5 spacing, 11-pt Calibri (Body) font, and 2 cm margins on all four sides of your page with appropriate section headings. • Reference sources must be cited in the text of the report and listed appropriately at the end in a reference list using the IEEE referencing style. • Late submission penalty: a penalty of 10% (of total available marks) per day, 0% score for more than 5 days late submission. • Students must ensure before submission of the final version of the assignment that the similarity percentage as computed by Turnitin must be less than 10%. Assignments with more than 10% similarity may not be considered for marking.
Extension	If an extension of time to submit work is required, a Special Consideration Application must be submitted directly on AMS. You must submit this application three working days prior to the due date of the assignment. Further information is available at: https://www.mit.edu.au/about-us/governance/institute-rules-policies-and plans/policies-procedures-and-guidelines/assessment-policy.
Academic Misconduct	Academic Misconduct is a serious offense. Depending on the seriousness of the case, penalties can vary from a written warning or zero marks to exclusion from the course or rescinding the degree. Students should make themselves familiar with the full policy and procedure available at: https://www.mit.edu.au/about-mit/institute-publications/policies-procedures-and guidelines/AcademicIntegrityPolicyAndProcedure. For further information, please refer to the Academic Integrity Section in your Unit Description.

2023 T1 Mathematical and Statistical Methods Page 2 of 6
Prepared by: Dr Anies Hannawati Moderated by: Prof Paul Kwan March, 2023
GROUP and SPARKPLUS
• This is a group assignment and groups will be formed randomly. Each group should consist of a maximum
of three students, with only the group leader responsible for submitting the required files through the
Moodle assignment submission link. It’s important to note that penalties will be applied if multiple
students submit highly similar files, as this can be considered plagiarism.
• SPARKPLUS is a tool for Self & Peer Assessment and Feedback for group assignments. It’s a mandatory task
for each student and the SPARKPLUS score will contribute towards determining the final assignment mark.
This score is calculated by the SPARKPLUS website as soon as all group members complete the review task.
To learn how to use SPARKPLUS, please refer to the Student’s SPARKPLUS Guideline in the assignment
folder. It’s important to note that failure to submit the SPARKPLUS review will result in a penalty mark
deduction.
ASSIGNMENT DESCRIPTION
In order to complete this assignment, students are required to compare the male and female populations in
their councils and ascertain whether there is a balance between the two. Additionally, students must forecast
their council population for the upcoming years. The population data for the hypothesis analysis and statistical
prediction tasks can be searched from the Australia Bureau of Statistics website http://www.abs.gov.au.
Task 1 Data Gathering [15 marks]
To complete the hypothesis analysis task, you will collect data on the male and female populations in your
council from the 2021 census. For example, if you reside in the Brimbank City Council in Melbourne, you should
collect data for males and females from postcodes 3020, 3021, 3022, 3023, 3036, 3037, and 3038. Additionally,
for the statistical prediction task, you will gather data on the total population of each council for the years
2001, 2006, 2011, 2016, and 2021.
As there are several members in your group, it is necessary to collect the same number of distinct sets of
council information population data for both the hypothesis and prediction tasks. In the event that more than
one member resides in the same council; a simple game of rock-paper-scissors can be played among the
members, with the winner selecting his/her preferred council first while the other member chooses a different
neighbourhood council at random. This approach will ensure that the data collected is diverse, representative,
and not duplicative.
As part of this report, your group will need to create one or more tables that represent the data you have
collected. Additionally, you should describe the process you have used to obtain the data from the relevant
website and provide a concise summary or explanation of the information presented in your tables.
Task 2 Descriptive Analysis [15 marks]
Once the relevant data has been collected, descriptive analysis can be used to summarise and examine the
characteristics of the dataset. This involves calculating key measures of central tendency and dispersion, such
as means, medians, and standard deviations, to better understand the data and determine what further
analysis can be performed. Additionally, graphical representations, such as histograms, box plots, and line
graphs, can be created to visually depict the data and identify patterns, trends, and outliers. In this second
task, students will utilise descriptive analysis techniques to extract valuable insights from the data collected in
Task 1. The insights gained through descriptive analysis will provide a comprehensive understanding of the
population characteristics of the council and inform subsequent statistical analysis and decision-making
processes.
2023 T1 Mathematical and Statistical Methods Page 3 of 6
Prepared by: Dr Anies Hannawati Moderated by: Prof Paul Kwan March, 2023
Task 3 Hypothesis Analysis [15 marks]
The aim of this task is to determine whether the male and female populations are balanced or if there are any
differences that can be found across the set of postcodes within the councils, and to identify which gender
has a higher population. To accomplish this objective, students must clearly state their hypothesis and conduct
hypothesis testing using appropriate statistical methods. The necessary data to answer this question can be
collected from three different councils, comprising various postcodes.
You will need to choose the suitable test, select the level of significance, compute the test statistic, and
interpret the results. You are free to use any software tool that you are comfortable with, including Python
and Excel, if appropriate. Upon completion of the hypothesis testing, you should provide a clear answer to the
question based on the results obtained. Whether you have found a significant difference or not, it is essential
to explain your answer and provide a detailed analysis of the findings.
Your submission should demonstrate a clear understanding of the hypothesis testing process, including the
steps involved, the statistical methods used, and the interpretation of the results. It is also important to
provide a thorough explanation of the findings and the implications of the results.
Task 4 Statistical Prediction [15 marks]
The aim of this task is to answer the question, “What is the projected total population of your councils in the
year 2050?” To achieve this goal, you will need to analyse patterns and relationships in your historical data
and make forecasts based on those insights. The necessary data to answer this question can be collected from
three different councils, comprising various postcodes.
To complete this task, you may use any software of your choice to identify the best-fit pattern or relationship
in the data that can be used to make accurate population projections. It is important to provide a detailed
step-by-step analysis, including the results and tests used to arrive at your final forecast.
Your submission should clearly demonstrate your understanding of the data and the forecasting process,
including any assumptions made, limitations, and the rationale behind the chosen method. The final forecast
should be supported by relevant data and statistical analysis to provide a reliable estimate of the total
population in your council in the year 2050.
Task 5 Presentation [10 marks]
A maximum 7-minute recorded video with all members participating in presenting the data analysis process,
results, and conclusions. It is recommended that the recorded video be uploaded to YouTube, but not directly
onto Moodle. Please include the URL to the YouTube video at the end of your written report.
FILE SUBMISSION
You need to submit three files in the Assignment 2 Submission Link:
1. Final report named YourGroupName.docx
The URL YouTube video link should be provided at the end of this written report.
2. Presentation PowerPoint file named YourGroupName.pptx
3. A Python Jupyter Notebook file or an Excel Workbook file, named YourGroupName.ipynb or
YourGroupName.xlsx
Do not forget to use the SPARKPLUS for Self & Peer Assessment and Feedback.
DEMONSTRATION/VIVA
During the laboratory and tutorial session in Week 11, your tutor will ask questions related to your assignment
report. You are expected to demonstrate your dataset processing, software, and calculation skills. It’s essential
that all group members attend and understand the entire assignment, as the viva questions will be marked
individually. If there are significant knowledge disparities within the group, individual marks will be awarded
based on each student’s ability to answer the questions.
2023 T1 Mathematical and Statistical Methods Page 4 of 6
Prepared by: Dr Anies Hannawati Moderated by: Prof Paul Kwan March, 2023
MARKING CRITERIA

Task	Description	Marks
Task 1 Data Gathering	• Population data for Task 1 is provided. • Population data for Task 2 is provided. • Written report regarding gathering the data and explanation of raw table or excel data.	15
Task 2 Descriptive Analysis	• Provide descriptive analysis of the data. • Further graphical representations are provided. • The calculations and explanations should clearly convey the meaning of the data.	15
Task 3 Hypothesis Analysis	• Hypothesis statement is clearly stated. • Perform the step hypothesis testing including choosing the suitable test, selecting the level of significance, and computing the test statistic. • Interpreting the results correctly and answering the given questions.	15
Task 4 Statistical Prediction	• Step-by-step explanation of how to find the best-fit model. • Answer the prediction population. • Discussion regarding the prediction, testing, etc.	15
Task 5 Presentation	• A maximum 7-minute recorded video with all members participating in presenting the introduction, dataset, data analysis process, results, and conclusions. • A functional YouTube URL link has been provided.	10
Reference Style and Presentation	• Follow IEEE reference style and should have both in-text citations and reference list. Use at least five resources for the assignment within the last 5 years. • Nice presentation of the report including format report, spelling, and grammar.	5 5
Demonstration/Viva	• Student should demonstrate their assignment which includes data gathering, hypothesis analysis, and statistical prediction. • The demonstration/viva will be conducted during the laboratory in Week 11.	20
Total	100
SPARKPLUS	RPF Factor
Final Marks = Total Marks x RPF Factor

2023 T1 Mathematical and Statistical Methods Page 5 of 6
Prepared by: Dr Anies Hannawati Moderated by: Prof Paul Kwan March, 2023
MARKING RUBRIC

Grades	>=80%	70%-79%	60% – 69%	50% – 59%	<50%
Task 1 Data Gathering	Student collected and presented data accurately, constructed a clear table/graph, explained data sources and provided an insightful summary.	Student collected and presented data well, constructed a table/graph effectively, explained data sources and provided a clear summary.	Student collected and presented data with minor errors, constructed a table/graph that has some issues with clarity, explained data sources and provided a basic summary.	Student attempted to collect and present data, but with significant errors, constructed a table/graph that has significant issues with clarity, explained data sources and provided a limited summary.	Student did not collect or present data accurately, construct an unclear table/graph, lack of explanation data sources or summary.
Task 2 Descriptive Analysis	Comprehensive understanding, effectively applies descriptive analysis, creates meaningful graphical representations , identifies patterns, trends, and outliers.	Good understanding, effectively applies descriptive analysis, creates graphical representations , identifies patterns, trends, and outliers.	Adequate understanding, applies descriptive analysis, creates graphical representations , identifies some patterns, trends, and outliers.	Basic understanding, some limitations in applying descriptive analysis, creates limited graphical representations , identifies some patterns, trends, and outliers.	Poor understanding, lacks ability to apply descriptive analysis, creates poor graphical representations , unable to identify patterns, trends, and outliers.
Task 3 Hypothesis Analysis	Student clearly stated the hypothesis, performed hypothesis testing accurately using appropriate statistical methods, and demonstrated a deep understanding of the hypothesis testing process.	Student stated the hypothesis clearly, performed hypothesis testing well using appropriate statistical methods, and demonstrated a good understanding of the hypothesis testing process.	Student stated the hypothesis with minor errors, performed hypothesis testing with some issues using appropriate statistical methods, and demonstrated a basic understanding of the hypothesis testing process.	Student attempted to state the hypothesis, but with significant errors, performed hypothesis testing with significant issues, and demonstrated a limited understanding of the hypothesis testing process.	Student did not state the hypothesis clearly, perform hypothesis testing inaccurately, and demonstrated a complete lack of understanding of the hypothesis testing process.

2023 T1 Mathematical and Statistical Methods Page 6 of 6
Prepared by: Dr Anies Hannawati Moderated by: Prof Paul Kwan March, 2023

Task 4 Statistical Prediction	Accurately analysed data, made a clear forecast, demonstrated deep understanding, provided detailed analysis, supported forecast with data, and explained assumptions and limitations.	Analysed data well, made a clear forecast, demonstrated good understanding, provided detailed analysis, supported forecast with some data, and explained some assumptions and limitations.	Analysed data with some issues, made a forecast with some inaccuracies, demonstrated basic understanding, provided basic analysis, and supported forecast with limited data and assumptions.	Attempted analysis with significant issues, and major inaccuracies in forecasting, demonstrated limited understanding, provided limited analysis, supported forecast with limited data or assumptions.	Did not analyse data accurately, inaccurate forecast, demonstrated no understanding, provided no analysis, did not support forecast with relevant data, did not explain assumptions and limitations.
Task 5 Presentation	Well-organised, engaging, all contribute equally, excellent quality, URL provided.	Adequately structured, all participate, satisfactory quality, URL provided.	Somewhat organised, all attempt to present, average quality, URL provided.	Lacks structure, not all participate, below average quality, URL provided.	Poorly structured, minimal participation, inadequate quality, no URL provided.
Reference Style and Presentation	Clear and excellent source of references. The report is presented professionally.	Clear referencing style. The report is written properly with some minor mistakes.	Generally good referencing style. The report is mostly good, but some presentation problems.	Unclear referencing style. The report is presented acceptably.	Lacks consistency with many errors. The report is presented carelessly with poor structure.
Demonstration /Viva	The student demonstrates full knowledge and provides an interesting presentation sequence of information that one can follow easily.	The student demonstrates good knowledge and provides a logical sequence of information that can be followed.	The student demonstrates average knowledge and provides an average logical sequence of presentation.	The student demonstrates average knowledge and provides no logical sequence of information with few mistakes.	The student demonstrates poor knowledge and provides no logical sequence of information with lots of mistakes.