Data analytics is a burgeoning field—with methods emerging quickly to explore and make sense of the huge amount of information that is being created every day. However, with any data set or analysis result, the primary concern is in communicating the results to the reader. Unfortunately, human perception is not optimized to understand interrelationships between large (or even moderately sized) sets of numbers. However, human perception is excellent at understanding interrelationships between sets of data, such as series, deviations, and the like, through the use of visual representations.
- Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27(1), 17–21.Google Scholar
- Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland, CA: Analytics Press.Google Scholar
- Playfair, W. (1801). “The statistical breviary; shewing, on a principle entirely new, the resources for every state and kingdom in Europe; illustrated with stained copper-plate charts, representing the physical powers of each distinct nation with ease and perspicuity. By William Playair”.Google Scholar
- Tufte, E. R., & Graves-Morris, P. R. (1983). The visual display of quantitative information (Vol. 2, No. 9). Cheshire, CT: Graphics Press.Google Scholar