Abstract
This chapter describes techniques for visualizing and quantifying bivariate and multivariate statistical data. Visualization techniques such as joint contingency tables, scatter plots, 2D histograms and line graphs are introduced. Numerical measures presented include covariance and correlation (both Pearson's and Spearman's correlation coefficients). The chapter describes how predictions of one variable can be made using knowledge of a second variable, based on regression analysis. Techniques for fitting best-fit straight lines and nonlinear curves are presented. Finally, Bland–Altman analysis is introduced as a way of visualizing the limits of agreement between two variables. To close, it is shown how MATLAB can be used to produce the visualizations and numerical measures introduced in the chapter.
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