The report focus on analysis of the time series data to identify any trends, seasonality, changing variance, behaviors, and change points. The stationarity of the time series data will be checked, and if it is found to be non-stationary, appropriate transformations and differencing will be performed to make it stationary. The report will use various model specification tools, such as ACF-PACF, EACF, and BIC table, to find possible ARIMA(p,d,q) models. The best model will be selected based on the model fit, and the parameters will be estimated accordingly.

This report will analyse the dataset containing the return (in AUD 100,000) of a share market trader’s investment portfolio and propose the best fitting model among Linear, Quadratic, Seasonal/ Cyclical and Cosine/ Harmonic by implementing models and using residual analysis. Based on the best model, prediction of next 15 trading days will be made.

The visualisation depicts the national statistics about victims from various offences in Australia through 1993 - 2021. And more detailed visualisation of crimes happening in 2021.

The objective of the analysis is to investigate the distribution in data for certain variables related to climate in Melbourne and Sydney. The variables considered are ‘Maximum temperature’, which records the highest temperature in a day, and ‘Solar exposure’, which records the total solar energy falling on a horizontal surface in a day. Both variables are inspected separately in both cities and the distribution of data is visualised and studied. The tests of normality include graphical visualizations such as histogram and density plots, QQ-plot and Shapiro-Wilk statistical test.

The document includes visualisation that need to improve from some perspectives such as data integrity, visual bombardment and perceptual or color issue. And, deconstructing and reconstructing to improve the original data visualisation.

The analysis focus on whether age and gender plays a role in having diabetes. The dataset is from (https://www.kaggle.com/datasets/alakaaay/diabetes-uci-dataset). Finding possible associations between age, gender and diabetes diagnosis as well as what are the age interval of diabetes patients are included in this. The chi square association test, two sample t test, one sample t test were used.