Scatterplots are a powerful tool for data visualization and analysis. They are used to display relationships between two or more variables and can be used to identify correlations, trends, and patterns. Scatterplots are a popular choice for data analysis due to their ability to quickly reveal relationships and trends that may not be clear in other graphical representations.
Scatterplots are typically used to demonstrate the linearity of the relationship between two variables, though other shapes may also be used. The variables are usually plotted on the x and y axes, and the points on the graph show the values of the variables for each observation. A line of best fit can be used to identify linear relationships, and the slope of the line can be used to calculate the correlation coefficient.
The data points in a scatterplot can be colored or labeled to distinguish between different groups or classes. This can be useful for identifying clusters or patterns in the data. Scatterplots can also be used to identify outliers, which are data points that lie far away from the main cluster.
Scatterplots are a useful tool for exploring data and identifying relationships. They can be used to identify correlations, trends, and patterns in the data. They can also be used to identify outliers and clusters, and to calculate the correlation coefficient.
References
Bandyopadhyay, S. S., & Suyal, M. (2019). Scatterplot. In Encyclopedia of Machine Learning and Data Mining (pp. 669-673). Springer, Cham.
Cleveland, W. S., & McGill, R. (1984). Graphical perception: theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531-554.
Healey, C. G. (2005). Exploring data with scatterplots. Scholastic Math, 25(4), 10-11.
Wilkinson, L. (2005). The grammar of graphics. Springer Science & Business Media.