Pearson Correlation Calculator
Measures the strength and direction of a linear association between two paired numeric variables. The worked condition keeps the method and source values visible for an independent check.
Enter the source values for the stated inputs
Pearson correlation
The quantity this page defines in the worked condition
Measures the strength and direction of a linear association between two paired numeric variables. The displayed relationship is r = cov(x,y)/(sx sy), and each symbol is tied to a labeled field. The page-specific quantity is pearson correlation.
The paired example gives a Pearson correlation of approximately 0.9878. This example is a numerical check of the method, not proof that the model describes every dataset. Before reusing pearson correlation, write down the observed scale, the model boundary, and the convention behind the displayed value. A short record of that kind makes it possible to distinguish a changed dataset from a changed definition, and it gives the next analyst a clear route back to the original calculation.
A neighboring method is spearman rank correlation.
Where the model applies before reporting
Correlation is not causation, and a strong value can hide curvature, outliers, or a restricted range. The page-specific quantity is pearson correlation.
The unit of analysis, exposure, sample boundary, and treatment of missing or tied values stay outside the answer unless they are explicitly entered. Keep those choices beside this regression result. The chosen pearson correlation convention remains attached to the source record. Before reusing pearson correlation, write down the observed scale, the model boundary, and the convention behind the displayed value. A short record of that kind makes it possible to distinguish a changed dataset from a changed definition, and it gives the next analyst a clear route back to the original calculation.
Keeping a reproducible record under the stated model
Check the scales and domains before evaluating pearson correlation. Counts, probabilities, rates, logarithms, and squared units are not interchangeable merely because a field accepts a number.
If one input changes, predict the direction of the result from the formula first. That simple check catches reversed groups, an incorrect parameterization, and percentage values entered on the wrong scale. The chosen pearson correlation convention remains attached to the source record.
What the statistic can support when the sample changes
Recalculate one intermediate quantity from r = cov(x,y)/(sx sy) and work back from the displayed answer. The source values should be sufficient for another analyst to reproduce pearson correlation without guessing a convention.
Use a boundary case when possible: equal paired values, a probability near zero, a zero slope, or a rate of zero. The expected limiting behavior is often more informative than another decimal place. The chosen pearson correlation convention remains attached to the source record.
The model’s reference quantity during an independent review
The number answers one statistical question. It does not establish causation, model fit, representativeness, or a useful decision threshold by itself. The page-specific quantity is pearson correlation.
Practical meaning depends on the measurement scale and consequences. State the comparison or benchmark before presenting pearson correlation as evidence.
Evidence beside the calculation at the chosen parameter values
Correlation is not causation, and a strong value can hide curvature, outliers, or a restricted range. Outliers, dependence, sparse observations, extrapolation, or a mismatched parameterization can change the appropriate reference method. The page-specific quantity is pearson correlation.
Choose an alternative because the design or data require it, not because its result is more favorable. Preserve the selected convention in the report. The chosen pearson correlation convention remains attached to the source record.
Questions about the method in this example
For this result, what should be checked before reusing this result?
Keep the inputs, units, model name, exclusions, and unrounded output together. The reported quantity here is pearson correlation.
When inputs change, why might another program return a different number?
Parameterization, tie rules, tail conventions, rounding, or distributional approximations can differ. The reported quantity here is pearson correlation.