PST107 Assessment 3 Brief Project Module 12 Page 3 of4 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. SLO d) Examine standard uni-variate distribution and their key properties SLO e) Relate data and represent data through data analysis SLO f) Interpret and estimate data using statistical methods and apply method of maximum likelihood estimation 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: 0 Univariate analysis - Bivariate analysis I Distribution analysis 0 Mean, Median, Standard deviation 0 Inference and parameter estimation 0 Correlation analysis I 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. 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 Exploratom 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 (Al) or Machine Learning (ML) to ensure that their results will be valid. In this assessment, we will be using Kagglecom (or other similar repositories recommended by your learning facilitator), which is an online community of Al, 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 I 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