Quantitative data analysis methods for air quality prediction

A quantitative research study gathers numerical data, which needs to be examined to make conclusions. Critical thinking regarding data analysis is facilitated primarily by quantitative data analysis. The purpose of data analysis is to identify the underlying trends, patterns, and connections in the context of a study. In quantitative data analysis, the focus is not just on applying statistical tests to existing data, but also on leveraging those tests as a means of deriving accurate insights from the data. This chapter aims to give a thorough theoretical review of current methodologies, related computational techniques, and dataset applications. A wide range of topics is covered, focusing on various statistical tests, interpolation, extrapolation, regression, modeling uncertainty analysis, machine learning methods, GIS techniques, data visualization tools etc. Each of these topics is extensively explored and discussed in terms of relevant literature and methods for air quality data and its applications.