MDA511 Assignment Help

Unit Code	MDA511
Unit Title	Mathematical and Statistical Methods
Term, Year	T2, 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 should be 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, 25th September 2023, 02:30 PM Week 11, Monday, 25th September 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:
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:

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 OBJECTIVE

The objective of this assignment is to give students hands-on experience in applying mathematical and statistical principles, including sampling methods, descriptive analysis, hypothesis testing, and predictive analytics, to analyse a real-world dataset. The dataset has been specifically chosen to help stakeholders make informed decisions. Students will determine whether and how to perform sampling, identify the research questions that can be answered using the data, and formulate hypotheses that can be tested to draw conclusions. In addition, students will perform predictive data analysis to forecast future trends based on the data.

ASSIGNMENT DESCRIPTION

To assist you in your analysis, a dataset containing information on temperature, humidity, rainfall, wind speed, wind direction, and wind force for weather station sites in Swan Hill Rural City has been provided. You can access this dataset in the assignment folder or download it directly from the following link: https://data.gov.au/data/dataset/swan-hill-rural-city-council-smart-cities-weather-stations. Your team has been assigned the task of analysing this dataset and presenting your findings through a written report and a presentation. As part of this assignment, you will need to apply mathematical and statistical principles, such as sampling methods, descriptive analysis, hypothesis testing, and predictive analysis, to explore the dataset.

The objective of your analysis is to generate insights that can aid researchers in making informed decisions based on the provided weather data. As data analysts, your team will need to determine whether and how to apply sampling techniques to the dataset. Additionally, you will need to identify specific research questions that can be answered by analysing the data and formulating hypotheses related to variations in weather data, including factors such as temperature, humidity, rainfall, and more. Furthermore, you may explore the potential for conducting predictive analyses to forecast weather patterns for upcoming months and years. By undertaking these analytical tasks, your team will contribute valuable insights to support decision-making processes and enhance understanding of the weather conditions in Swan Hill Rural City. Note that you are free to use any software tool that you are comfortable with, including Python (Jupyter Notebook) and Excel, if appropriate. You will also need to conduct additional research and include at least five resources from a combination of journals, conference papers, websites, or other reliable sources to support your analysis. It is important to ensure that all sources are published or updated within the last five years (2018-2023) to ensure they are relevant and up to date.

Task 1 Data Gathering/Sampling Method [15 marks]

To begin, carefully examine the provided dataset and identify two consecutive months with complete dates. Clean up any unused data from the dataset. Choose at least three variables, including temperature data. Once your selections are made, determine the appropriate sampling method that aligns with the chosen data points. Proceed to gather the necessary data based on your selected sampling approach. In the subsequent sections of your report, your group will need to construct one or more tables that effectively present the collected data. These tables should accurately organize and represent the selected data points. Additionally, it is important to describe the process used to obtain the data, including the steps taken and any relevant sources utilised.

Task 2 Descriptive Analysis [15 marks]

Once you have gathered the relevant data, of your it is essential to employ descriptive analysis techniques to effectively summarise and examine the dataset’s characteristics. This involves calculating key measures of central tendency, such as means and medians, as well as measures of dispersion, including standard deviations. By doing so, you can gain a deeper understanding of the data and identify opportunities for further analysis. In addition to numerical summaries, it is beneficial to create graphical representations to visually depict the data. Histograms, box plots, and line graphs are examples of visual tools that can effectively highlight patterns, trends, and outliers within the dataset. These graphical representations provide valuable insights and facilitate a comprehensive understanding of the weather characteristics specific to the council.

For this second task, students will utilise descriptive analysis techniques to extract meaningful insights from the data collected in Task 1. These insights will play a vital role in informing subsequent statistical analysis and decision-making processes, allowing for a well-rounded understanding of the council’s weather patterns and characteristics.

Task 3 Hypothesis Analysis [15 marks]

The aim of this task is to determine whether there is a significant difference in the average values of a chosen variable between consecutive months. Select one variable that you are most interested in, such as temperature. If temperature is chosen, the task is to determine whether there is a significant difference in the average temperature between two consecutive months and identify which month has a higher temperature.

To achieve this objective, students must clearly state their hypothesis and perform hypothesis testing using appropriate statistical methods. You will need to choose the suitable test, select the level of significance, compute the test statistic, and interpret the results. 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 Linear Regression and Statistical Prediction [15 marks]

First, utilise Linear Regression to determine whether there is a correlation between temperature and other variables, considering data from the first month only. Develop a mathematical model for the temperature using either temperature data alone or in combination with other variables, depending on your preference. Next, use the data from the second month to assess the accuracy of your best-fit model. To complete this task, you are free to use any software of your choice to identify the best-fit pattern or relationship in the data that can be used to make accurate temperature projections. Provide a detailed step-by-step analysis, including the results and tests employed to arrive at your final model. Your submission should demonstrate a clear understanding of the data and the forecasting process, incorporating any assumptions made, limitations, and the rationale behind the chosen method.

Task 5 Presentation [10 marks]

To fulfill the assignment requirements, your team is tasked with creating a concise, maximum seven-minute video presentation where all team members present the key points of the data analysis processes and results. The recorded video should be uploaded to YouTube as an unlisted file, ensuring that only viewers with the link can access it. Remember to include the link to the YouTube video at the end of your written report.

FILE SUBMISSION

The group leader needs to submit three files in the Assignment 2 Submission Link:

Final report named YourGroupName.docx

The URL YouTube video link should be provided at the end of this written report.

Presentation PowerPoint file named YourGroupName.pptx
A Python Jupyter Notebook file or an Excel Workbook file, named YourGroupName.ipynb or YourGroupName.xlsx

Each individual student must submit 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.

Task	Description	Marks
Task 1 Data Gathering/Sampling Method	All datasets for two consecutive months are provided. Written report regarding gathering the data or sampling method 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 Linear Regression and Statistical Prediction	Linear regression analysis is provided. Step-by-step explanation of how to find the best-fit model. Testing data is provided. 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

Grades	>=80%	70%-79%	60% – 69%	50% – 59%	<50%
Task 1 Data Gathering/Sampling Method	Student collected and presented data and sampling method accurately, constructed a clear table/graph, explained data sources and provided an insightful summary.	Student collected and presented data and sampling method well, constructed a table/graph effectively, explained data sources, and provided a clear summary.	Student collected and presented data and sampling method 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 and sampling method, 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 and sampling method 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.
Task 4 Linear Regression and Statistical Prediction	Accurately analysed data, provided linear regression and best-fit model, made clear test data for forecast, demonstrated deep understanding, provided detailed analysis, and explained assumptions and limitations.	Analysed data well, provided linear regression and best-fit model, made a clear testing for 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.