ASSESSMENT 3: PROJECT BRIEF

Subject Code and Title

PST107: Probability and Statistics
Assessment 3: Problem Based Data Analysis Project Individual
Project and supporting document

Assessment

Individual/Group

Length

Learning Outcomes

1. a)  Recognise the fundamentals of probability theory and its application
2. b)  Identify probability laws via Bayes’ rule and use method of moments generating functions
3. c)  Explain the concepts of statistical distribution and statistical inference.
4. d)  Examine standard uni-variate distribution and their key properties
5. e)  Relate data and represent data through data analysis
6. f)  Interpret data using statistical methods using statistical tools and apply method of maximum likelihood estimation

Submission

Due by 11:55pm AEST Sunday end of Module 12. 35%
100 marks

Weighting

Total Marks

Context

This assessment has been designed to assess your ability of choosing and employing the best statistical methods and tools to analyse simplified real-world datasets. You will be playing the role of a data scientist in this assessment to perform Exploratory Data Analysis (EDA). EDA allows data scientists to summarise the main characteristics of a dataset, which is usually done using a wide range of statistical approaches and visualisations. EDA is also very helpful for analysing data before using any Artificial Intelligence (AI) or Machine Learning (ML) to ensure that their results will be valid.

In this assessment, we will be using Kaggle.com (or other similar repositories recommended by your learning facilitator), which is an online community of AI, ML, and data science experts and enthusiasts. You are supposed to thoroughly analyse one of the datasets in this website using the statistical techniques that you learned in this subject.

PST107 Assessment 3 Brief Project Module 12 Page 1 of 4

Task Summary

As mentioned above, you have to first choose a dataset from Kaggle or other similar websites

recommended by your learning facilitator (e.g. Heart Disease UCI). As the only limitation, the dataset

must have at least four columns (features). To avoid similar submissions, the dataset should be

checked with your learning facilitator. In some trimesters, the learning facilitator might assign you a

specific dataset. After the approval of the dataset you will then have to analyse the dataset using the

following statistical methods:

       

Univariate analysis

Bivariate analysis

Distribution analysis

Mean, Median, Standard deviation

Inference and parameter estimation

Correlation analysis

Hypothesis testing

Visualisation

When you finish applying these methods, you are required to write a document with a minimum of

1500 words to explain the statistical methods you used and the conclusion that can be drawn from

your dataset based on the methods you applied. You are writing this report for someone who wants

to understand this dataset. In other words, you are trying to tell a story with data, analysis, and

visualisation. Please do not forget to use as many visualisations as you can to make your report more

engaging.

Submission Instructions

Submit Assessment 3: Problem Based Data Analysis Project via the Assessment link in the main navigation menu in PST107 Probability and Statistics. The Learning Facilitator will provide feedback via the Grade Centre in the LMS portal. Feedback can be viewed in My Grades.

Do not leave this assessment to the last minute!

Be sure to keep several drafts of your work as well as your notes and any sources you used to draw on in preparing your report. Extensions will be considered only in extenuating circumstances where the student has applied before the due date. At that point, students are required to provide the latest draft, in case the extension is not granted and to demonstrate they have earnestly done everything to avoid lateness.

You are responsible for keeping appropriate back-ups and drafts of their assignments and to submit the correct version.

Torrens University Australia policies apply to the preparation and submission of this assignment.

Upon the completion of your experiments and report, you have to submit the report in the system.

Please note that your submission should include the files generated in the software that you use

(e.g. SPSS, R, …) and the report.

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Assessment Rubric

Assessment Attributes	Fail (Yet to achieve minimum standard) 0-49%	Pass (Functional) 50-64%	Credit (Proficient) 65-74%	Distinction (Advanced) 75-84%	High Distinction (Exceptional) 85-100%
Work demonstrates the knowledge and understanding of the univariate and multivariate analyses 20%	Little or no use of univariate and multivariate analyses	Acceptable use of univariate and multivariate analyses The analyses are correct but includes errors and flaws	Good use of univariate and multivariate analyses The analyses are done correctly, but the main aspects of the dataset are not investigated	Very good use of univariate and multivariate analyses The analyses are conducted correctly but are not used cohesively in the document.	Excellent use of univariate and multivariate analyses The analyses are flawless and used exceptionally to tell a cohesive story in the document
Work demonstrates the knowledge and understanding of distribution analyses 20%	Little or no use of distribution analyses	Acceptable use of distribution analyses The analyses are correct but includes errors and flaws	Good use of distribution analyses The analyses are done correctly, but the main aspects of the dataset are not investigated	Very good use of distribution analyses The analyses are conducted correctly but are not used cohesively in the document.	Excellent use of distribution analyses The analyses are flawless and used exceptionally to tell a cohesive story in the document
Work demonstrates the knowledge and understanding of expectation analyses (mean, median, std, etc.) 20%	Little or no use of expectation analyses	Acceptable use of expectation analyses The analyses are correct but includes errors and flaws	Good use of expectation analyses The analyses are done correctly, but the main aspects of the dataset are not investigated	Very good use of expectation analyses The analyses are conducted correctly but are not used cohesively in the document.	Excellent use of expectation analyses The analyses are flawless and used exceptionally to tell a cohesive story in the document
Work demonstrates the knowledge and understanding of inference and parameter estimation 20%	Little or no use of inference and parameter estimation	Acceptable use of inference and parameter estimation The analyses are correct but includes errors and flaws	Good use of inference and parameter estimation The analyses are done correctly, but the main aspects of the dataset are not investigated	Very good use of inference and parameter estimation The analyses are conducted correctly but are not used cohesively in the document.	Excellent use of inference and parameter estimation The analyses are flawless and used exceptionally to tell a cohesive story in the document
Work demonstrates the knowledge and understanding of correlation analysis and Hypothesis testing 20%	Little or no use of distribution analyses and tests	Acceptable use of correlation analysis and Hypothesis testing The analyses and tests are correct but includes errors and flaws	Good use of correlation analysis and Hypothesis testing The analyses and tests are done correctly, but the main aspects of the dataset are not investigated	Very good use of correlation analysis and Hypothesis testing The analyses and tests are conducted correctly but are not used cohesively in the document.	Excellent use of correlation analysis and Hypothesis testing The analyses and tests are flawless and used exceptionally to tell a cohesive story in the document

PST107 Assessment 3 Brief Project Module 12 Page 3 of 4

SLO a) SLO b) SLO c) SLO d) SLO e) SLO f)

The following Subject Learning Outcomes are addressed in this assessment

Recognise the fundamentals of probability theory and its application
Identify probability laws via Bayes’ rule and use method of moments generating functions
Explain the concepts of statistical distribution and statistical inference.
Examine standard uni-variate distribution and their key properties
Relate data and represent data through data analysis
Interpret and estimate data using statistical methods and apply method of maximum likelihood estimation

PST107 Assessment 3 Brief Project Module 12

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