The information age has resulted in masses of data in many domains(e.g.,demographic analysis, physical simulations, biochemical data or environmental data analysis) which has multivariate properties. How can this type of data be explored to enable analysis and reveal pattern and features? One way is to visualize data correlations through a scatter plot. The project aims to develop an extended scatter plot in the GAV framework a component-based class library developed in Microsoft’s C# .NET platform using low-level DirectX graphics library. GAV uses an atomic component approach to increase customization and scalability of the scatter plot and depicts a layer where a specific idea or task is implemented. The scatter plot consists of several such layers.

Some of the implemented tasks to improve a basic scatter plot is adding support for four numerical dimensions, to fit a linear least square regression line showing bivariate relationship explicitly, to allow the user to control focus and context view, to reveal internal characteristics and distribution of data based on percentile calculation, to incorporate multivariate interpolation, a process of assigning values to unknown points by using values from usually scattered set of known points. Intuitive interaction is also one of the goals of the thesis. Relevant snapshots with implementation details are provided as results and several ideas are mentioned for mproving and developing further.