Calculator
What the Correlation Calculator does
Correlation Calculator: calculates Pearson r from X values (comma-separated) and Y values (comma-separated). Example: x values (comma-separated) 1, 2, 3, 4, 5 and y values (comma-separated) 2, 4, 5, 4, 5 gives pearson r 0.774597.
With X values (comma-separated) and Y values (comma-separated) entered, the Correlation Calculator works out Pearson r. Adjust anything and the figures re-derive on the spot.
Most people who open the Correlation Calculator are someone sanity-checking a spreadsheet. The method matters as much as the answer when you are still learning the topic.
The page holds your values in memory and forgets them the moment you close it.
Field definitions, the method, and a worked example from the default values are all set out further down the page.
What do the Correlation Calculator fields mean?
The Correlation Calculator uses 2 inputs. A filled form means you can test one idea immediately instead of sourcing every input first.
| Field | What to enter | Default |
|---|---|---|
| X values (comma-separated) | required | 1, 2, 3, 4, 5 |
| Y values (comma-separated) | required | 2, 4, 5, 4, 5 |
How does the Correlation Calculator work?
Each output is derived from the inputs above in a single pass; there is no hidden state carried between runs, so the same inputs always give the same calculation.
Bad input produces a message, not a silently incorrect figure.
Worked example
These are the values the Correlation Calculator loads by default, and the result it produces from them. Below is the same computation the tool performs on load, step by step.
| X values (comma-separated) | 1, 2, 3, 4, 5 |
|---|---|
| Y values (comma-separated) | 2, 4, 5, 4, 5 |
| Pearson r | 0.774597 |
|---|---|
| What this means | Pearson r ≈ 0.7746 (−1 to +1) |
Beyond the formula: Pearson correlation
Pearson’s r measures only linear association between two variables. It can sit close to zero even when the variables are strongly related in a curved, non-linear way, which is the single most common misreading of a correlation result.
Common mistakes and sanity checks
- Correlation does not imply causation: two variables can move together because one causes the other, because both are driven by a third factor, or by pure coincidence. r alone can never distinguish between these.
- r ranges from −1 (perfect inverse relationship) to +1 (perfect direct relationship), with 0 meaning no linear relationship. There is no universal cutoff for what counts as "strong": that depends on the field.
- A well-known cautionary example (Anscombe’s quartet) shows four datasets with nearly identical correlation coefficients but wildly different shapes when actually plotted, always look at a scatter plot alongside the number, not instead of it.
How to use it
- Type X values (comma-separated).
- Enter Y values (comma-separated).
- No submit step is needed; the output updates while you edit.
- Look at Pearson r and What this means in the output panel.
- Copy Link when you want to return to this exact configuration; Print when someone needs it on paper.
What this tool does not do
- Very large or very small numbers hit JavaScript floating-point limits (about 15 significant digits).
- Displayed values are rounded; the underlying calculation keeps full precision.
- Precision beyond what the inputs justify is not meaningful, however many digits appear.
Frequently asked questions
The form asks for X values (comma-separated) and Y values (comma-separated). Every box already holds a working value, so nothing is mandatory before you see a result.
With the values loaded when the page opens, pearson r comes out as 0.774597. Adjust any field to see how much the answer depends on it.
Not at all: the whole thing runs offline once the page has loaded, which tells you nothing is being sent.
No charge. Everything on the page is available without signing in.
As accurate as the inputs allow. The arithmetic itself introduces no error beyond display rounding.