Understanding Statistical Correlations With R Programming Functions

Correlation is an essential step in regression analysis. With R programming there are more ways to identify correlations among variables and display a matrix of correlation values. Here’s how.

When looking at a dataset, you have to look at the relationships among the data that are meant to be variables. Usually, the variables are in columns. While Exploratory Data Analysis looks at the observations, you need a tool to help understand the relationships among the columns. That’s where a correlation should be measured. Doing so establishes the development of a regression model by indicating which variables have correlated behavior from their observations. The strength of the correlations is a boon for reviewing advanced data models such as a Principle Component Analysis (PCA).

In this post, I will explain what a correlation is, what it means for your analysis, and what functions in R programming can help measure the correlation among your variables.

What is a correlation?

Correlation is a measured indication of the relationship among two variables of interest within a data model. It is a statistical scale, meant to…