# What is the r correlation coefficient

If it went through every point then I would have an R of one but it gets pretty close to describing what is going on. The correlation coefficient is a measure of how well a line can describe the relationship between X and Y. Current time: The sample mean for X is quite straightforward to calculate, it would just be one plus two plus two plus three over four and this is eight over four which is indeed equal to two. The above figure shows examples of what various correlations look like, in terms of the strength and direction of the relationship. Different relationships and their correlation coefficients are shown in the diagram below:.

## Calculating correlation coefficient r

Pearson's correlation determines the degree to which a relationship is linear. Many folks make the mistake of thinking that a correlation of —1 is a bad thing, indicating no relationship. The sample mean for Y, if you just add up one plus two plus three plus six over four, four data points, this is 12 over four which is indeed equal to three and then the sample standard deviation for Y you would calculate the exact same way we did it for X and you would get 2. However, you can take the idea of no linear relationship two ways: This is shown in the diagram below: What were we doing? I'll do it like this. Remember that these values are guidelines and whether an association is strong or not will also depend on what you are measuring. Indeed, the calculations for Pearson's correlation coefficient were designed such that the units of measurement do not affect the calculation.

Two minus two, that's gonna be zero, zero times anything is zero, so this whole thing is zero, two minus two is zero, three minus three is zero, this is actually gonna be zero times zero, so that whole thing is zero.

Does the Pearson correlation coefficient indicate the slope of the line?

## Pearson Product-Moment Correlation

There is a linear relationship between the two variables but see note at bottom of page. Main content. This allows the correlation coefficient to be comparable and not influenced by the units of the variables used. Well, these are the same denominator, so actually I could rewrite if I have two over this thing plus three over this thing, that's gonna be five over this thing, so I could rewrite this whole thing, five over 0.

If you have ordinal data, you will want to use Spearman's rank-order correlation or a Kendall's Tau Correlation instead of the Pearson product-moment correlation. So, before I get a calculator out, let's see if there's some simplifications I can do. The variables must be approximately normally distributed see our Testing for Normality guide for further details.