CORREL measures how strongly two sets of numbers move together. Near 1 means a strong positive relationship. Near -1 means an inverse relationship. Near 0 means no relationship.
CORREL calculates the Pearson correlation coefficient between two sets of data — a number between -1 and 1 that measures the strength and direction of the linear relationship between them. It is widely used in data analysis, financial modelling, HR analytics, and scientific research.
=CORREL(array1, array2)| Argument | Description |
|---|---|
| =CORREL(array1, array2) | |
| array1 required | The first dataset — a range of numbers. |
| array2 required | The second dataset — must be the same size as array1. |
=CORREL(B2:B13, C2:C13)Returns a value from -1 to 1.
=CORREL(A2:A365, B2:B365)=IF(CORREL(B2:B50,C2:C50)>0.7,"Strong positive","Weak or negative")Flag strong correlations automatically.
=CORREL($B$2:$B$100, C$2:C$100)Lock column B and copy right to build a correlation row.
=CHOOSE(MATCH(ABS(CORREL(B2:B50,C2:C50)),{0,0.3,0.7,0.9},1),"Weak","Moderate","Strong","Very strong")Returns a text label for the correlation strength.
The CORREL result ranges from -1 to +1. A value of +1 means a perfect positive correlation — every time one variable increases, the other increases by a proportional amount. A value of -1 means a perfect negative correlation — as one rises, the other falls by a proportional amount. A value of 0 means no linear relationship exists between the two variables.
In practice, values are rarely exactly -1, 0, or +1. A general guide: 0 to 0.3 is a weak relationship, 0.3 to 0.7 is moderate, 0.7 to 1.0 is strong. These thresholds vary by field — a correlation of 0.4 might be considered strong in social science but weak in engineering.
Correlation only measures LINEAR relationships. Two variables can have a strong curved relationship and still produce a CORREL result near 0. Always plot your data in a scatter chart alongside the CORREL result to check whether the relationship actually looks linear.
CORREL tells you that two variables move together — it does not tell you why. Ice cream sales and drowning incidents are positively correlated (both increase in summer) but one does not cause the other. Always apply domain knowledge when interpreting correlation results.
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