# Scatterplot R2 linear result [closed]

If I have an R2 linear result of .004 showing up on my scatterplot, what does it mean? I am having a difficult time interpreting my scatterplot.

## closed as unclear what you're asking by Roland, Michael Chernick, kjetil b halvorsen, whuber♦May 28 '17 at 14:52

Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

• Since in most models $R^2$ can range from $0$ through $1$, $0.004$ is essentially zero, meaning you might as well consider your variables to be uncorrelated. We have a few thousand posts that discuss what a correlation of zero might mean and how it can arise: please visit the links at stats.stackexchange.com/search?q=zero+correlation. – whuber May 28 '17 at 14:52

## 1 Answer

You can easily find an answer to this question by simply using google or a basic statistics textbook... For example, I based this answer on a chapter from a book called Fundamental Statistics for the Behavioural Sciences by Howell (2013).

R2, the squared correlation coefficient, explains the strength of the relationship between the two variables in your scatter-plot. Say you have two variables, X (predictor) and Y (outcome), there is a lot of variability in Y. Some of that variability will be related to your predictor variable, X, but some will also be noise, also referred to as error.

If X (say height) is a good predictor of Y (say weight), then a lot of the variability in height will be associated with variability in weight. This essentially means some of the reason as to why people vary in weight will be because people differ in their height. So part of the reasons why people differ in weight is because they differ in how tall they are.

You can consider R2 as:

R2 = variation in Y (in our example weight) explained by X (in our example height) / Variation in Y (weight)

Given the equation above, R2 equals the percentage of the variability in weight (Y), that height (X) is able to predict or explain. In your case, the R2 value means that your predictor explains less than 1% of the variability in your outcome variable.