Overview
Plots play an important role in statistics and data analysis. The procedures here can broadly be split into two parts: quantitative and graphical. Quantitative techniques are the set of statistical procedures that yield numeric or tabular output. Examples of quantitative techniques include:
- hypothesis testing
- analysis of variance
- point estimates and confidence intervals
- least squares regression
These and similar techniques are all valuable and are mainstream in terms of classical analysis. There are also many statistical tools generally referred to as graphical techniques. These include:
- scatter plots
- histograms
- probability plots
- residual plots
- box plots, and
- block plots
Graphical procedures such as plots are a short path to gaining insight into a data set in terms of testing assumptions, model selection, model validation, estimator selection, relationship identification, factor effect determination, outlier detection. Statistical graphics give insight into aspects of the underlying structure of the data.
Graphs can also be used to solve some mathematical equations, typically by finding where two plots intersect.
Read more about this topic: Plot (graphics)