Correlation Coefficient (R) Calculator
Find Correlation coefficient between 2 variables using Pearson correlation formula (R). Generate Scatter Plot using the given set of data
Correlation Statistics
Metric | Formula | Description | Result |
|---|---|---|---|
Pearson Correlation Coefficient / R Correlation | Correlation Coefficient Formula: | where, x̄ is the mean of x, ȳ is the mean of y and xi,yi are the individual values in sets of x and y | 0.9424 |
Count of dataset 1 | Count = n | where, n is the Number of items in dataset 1 | 18 |
Count of dataset 2 | Count = n | where, n is the Number of items in dataset 2 | 18 |
Sample Std Deviation | S = Square root((1/ n - 1 ) * ∑ i( x i - x ¯ ) 2) | where, n is Number of observations in sample, xi is individual values in sample and x ¯ is Sample mean | 6.546 |
XY Scatter Plot
Data shows Strong Positive Correlation
Correlation Analysis / Inference
Correlation Range | Description |
|---|---|
1 | 100% Positive Correlation |
>= 0.7 < 1 | Strong Positive Correlation |
>= 0.5 < 0.7 | Moderate Positive correlation |
>= 0.3 < 0.5 | Low Positive Correlation |
> 0 <= 0.3 | No Correlation |
0 | No Correlation |
< 0 >= -0.3 | No Correlation |
>= -0.3 < -0.5 | Low Negative Correlation |
>=-0.5 < -0.7 | Moderate Negative Correlation |
>= -0.7 < - 1 | Strong Negative Correlation |
-1 | 100% Negative Correlation |
Significance of R
Significance of R is a measure of reliability. It is used to indicate if there is significant linear relationship between the variables and if it can be used for predictions
If R is significant, it indicates that the coefficient value and Scatter plot can be used for prediction of outcomes.based on the significant linear relationship between 2 variables
If R is NOT SIGNIFICANT, it means insignificant relationship between the variables and it cannot be used reliably to predict any outcome
This is used as a pre-requisite test before applying the Pearson's coefficient formula and analyzing
Frequently Asked Questions on Correlation Coefficient Calculator
Correlation Coefficient is the strength and relationship between two variables and the direction of it (+ve or -ve).
Age vs Height have a positive correlation during the growing period.
Calories Consumed vs Weight have a positive correlation.
Relative Humidity and Temperature have a negative correlation
R stands for Regression and is the correlation Coefficient between 2 sets of variables.
It is a quantitative measure representing linear correlation coefficient.
It indicates whether the 2 sets of data have positive, negative or no correlation.
correlation coefficient r = Σ(xi - x̄)(yi - ȳ) / √(Σ(xi - x̄)2Σ(yi - ȳ)2 ).
where,where, x̄ is the mean of x, ȳ is the mean of y and xi,yi are the individual values in sample sets of x and y.
It ranges between -1 to 1
100% Positive Strong Correlation
100% Negative Strong Correlation
No Correlation
Significance of R is a measure of reliability.
It is used to indicate if there is significant linear relationship between the variables and if it can be used for predictions.
If R is significant, it indicates the trend line can be used for prediction of outcomes.based on the significant linear relationship between 2 variables
If R is NOT SIGNIFICANT, it means insignificant relationship between the variables and it cannot be used reliably to predict any outcome
1
1 (positive) and -1 (negative)
Strong Positive Correlation
Moderate Positive correlation
No Correlation
1 (positive) and -1 (negative)
Less than 0.5
weak correlation
strong correlation
