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Correlation expresses how much two variables relate to each other **statistically**. To be able to express this, we can use various coefficients and relationships. The most frequently used correlation relationship is Pearson’s, whose value determines to what extent two variables are **linearly** dependent.

If the correlation has a value of 1, it means perfect correlation. If it is 0, this means the values do not correlate to each other.

As an example of correlation close to one, consider the monthly average rainfall during the year in Prague and in the Canadian city of Calgary. On its horizontal axis, the left graph shows the time of the year, and the vertical axis shows the average rainfall in individual months.

On the right graph, plotted on the horizontal axis we see the rainfall in Prague, and on the vertical axis we see the rainfall in Calgary. Every point in the right graph represents a month of the year.

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