The aim of this investigation is to analyse and forecast the Industrial production of electric and gas utilities (IP index) for the next 10 months.

Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions, precipitation, day of week, season and other factors can affect the rental behaviours. We will attempt to fit a multiple linear regression model, then evaluate the adequacy of the fitted model and rebuild for a better model.

The aim of the investigation is to find the best fitting trend model to a provided Ozone time series data and to make predictions of yearly changes for the next 5 years.

The problem of this investigation is to determine if one of the body measurements fits a normal didtribution.

Understanding the relationship between two quantitative variables (Infant Mortality Rate (IMR) and GDP per capita) in order to allow making accurate predictions. The investigation problem will be solved by using linear regression model for modelling the relationship between the variables of interest. Correlation will be used to evaluate the strength and the direction of this relationship (if any). These methods assume that a predictor variable, GDP per carita, provides information about a dependent variable, infant mortality rate.

Data preprocessing tasks on two datasets

Analysis and forecasting of Solar radiation series. Analysis of the correlation between Residential Property Price Index (PPI) in Melbourne and population change in Victoria from September 2003 to December 2016.

Data preprocessing tools on open data sets