Correlation is the exploratory step between describing variables individually and fitting a regression model. Which variables are related? How strongly? Is the relationship linear, monotonic, or something else entirely? A coefficient alone won’t tell you — a strong Pearson r can hide a non-linear pattern, and a weak r can mask a relationship that’s clear in a subgroup but absent in another.
You need both the numbers and the pictures. Pearson, Spearman, and Kendall coefficients with proper confidence intervals give you the strength and significance. Colour-mapped correlation matrices and scatter plot matrices show you the patterns, the clusters, and the outliers that a single number misses.
See correlation results in detail — colour-mapped matrices, scatter plot matrices, and pairwise coefficients with CIs.
Correlation is one part of a complete statistical analysis toolkit. The Standard edition also includes ANOVA and ANCOVA, simple and multiple regression, logistic regression, PCA and factor analysis, descriptive statistics, hypothesis testing, and categorical data analysis. See everything in the Standard edition →