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Know your problem
Know your data
Visualize your data – numerical variables
Work with your chart
12. Correlation
Check yourself


We can deduce something about our data by analyzing the position of the dots. But can we say for sure that a relationship exists? Or do we just have random dots?

One way to establish a relationship is to measure it.

There are many ways to measure the relationship between data. The most popular is to establish the 'correlation' – the linear relationship – between the data values. There are nonlinear relationships as well, but we'll focus on the simpler linear ones in this course.

When we are discussing correlations, we can talk about:

  • A positive correlation, where both variables' values increase together.
  • A negative correlation, where the values of one variable decrease when another variable's values increase.
  • No correlation – points are arranged more or less randomly.


Correlation can be measured by the "correlation coefficient" (r). The exact formula for r isn't crucial for this course, but you can find it in our Statistics course. All you need to know about r is:

  • It ranges from -1 to 1. Values above zero correspond a positive correlation, while values below zero show a negative correlation. Values at or very near zero mean no correlation.
  • The closer the r value is to 1, the stronger the positive correlation is. The closer it is to -1 , the stronger the negative correlation is.

In our example, the correlation coefficient is 0.6, so it's a pretty high positive correlation.