In general, for the coefficients Pearson's r and Spearman's ρ, values from 0 to 0.3 (or 0 to -0.3) are biologically negligible; those from 0.31 to 0.5 (or -0.31 to -0.5) are weak; from 0.51 to 0.7 (or -0.51 and -0.7) are moderate; from 0.71 to 0.9 (or -0.71 to 0.9) are strong correlations; and correlations > 0.9 (or < ...
Values between 0.3 and 0.7 (0.3 and −0.7) indicate a moderate positive (negative) linear relationship through a fuzzy-firm linear rule. Values between 0.7 and 1.0 (−0.7 and −1.0) indicate a strong positive (negative) linear relationship through a firm linear rule.
However, coefficients with an absolute value of <0.40 are usually interpreted as indicating weak correlations; those with a value of 0.40–0.69, moderate correlations; and those with a value of ≥0.70, strong correlations.
Correlation coefficients whose magnitude are between 0.7 and 0.9 indicate variables which can be considered highly correlated. Correlation coefficients whose magnitude are between 0.5 and 0.7 indicate variables which can be considered moderately correlated.
If we wish to label the strength of the association, for absolute values of r, 0-0.19 is regarded as very weak, 0.2-0.39 as weak, 0.40-0.59 as moderate, 0.6-0.79 as strong and 0.8-1 as very strong correlation, but these are rather arbitrary limits, and the context of the results should be considered.
A correlation coefficient of 0.7 indicates a significant positive correlation between two variables. This means that instances of the first variable increasing (i.e. ice cream sales) are a strong indicator of the second variable increasing (i.e. shark attacks).
r values ranging from 0.50 to 0.75 or -0.50 to -0.75 indicate moderate to good correlation, and r values from 0.75 to 1 or from -0.75 to -1 point to very good to excellent correlation between the variables (1).
You can have a Pearson's correlation coeficent almost zero (r=0.07) but significant (p=0.001), with a large sample size (n=2160). It is significant but is spurious, it doesn't have any practical application.
Correlation Coefficient = 0.8: A fairly strong positive relationship. Correlation Coefficient = 0.6: A moderate positive relationship. Correlation Coefficient = 0: No relationship. As one value increases, there is no tendency for the other value to change in a specific direction.
This indicates that two variables are both increasing (or decreasing). This value, +0.74, is a strong positive correlation. For example, high spending, more debt, versus low spending, low debt.
For some people anything below 60% is acceptable and for certain others, even a correlation of 30% to 40% is considered too high because it one variable may just end up exaggerating the performance of the model or completely messing up parameter estimates.
This is interpreted as follows: a correlation value of 0.7 between two variables would indicate that a significant and positive relationship exists between the two.
Correlation coefficients whose magnitude are between 0.7 and 0.9 indicate variables which can be considered highly correlated. Correlation coefficients whose magnitude are between 0.5 and 0.7 indicate variables which can be considered moderately correlated.
The correlation coefficient, r, measures the strength of the linear association between two variables. A correlation coefficient of -0.8 indicates a stronger negative correlation than a correlation coefficient of 0.72, which is a weak positive correlation.
Labeling systems exist to roughly categorize r values where correlation coefficients (in absolute value) which are ≤0.35 are generally considered to represent low or weak correlations, 0.36 to 0.67 modest or moderate correlations, and 0.68 to 1.0 strong or high correlations with r coefficients ≥ 0.90 very high ...
Definition: The correlation coefficient measures the strength and direction of a linear relationship between two variables. A value of 0.7 indicates a strong positive correlation, meaning that as one variable increases, the other tends to also increase.
A linear correlation coefficient of 0.85 for a paired data set indicates that there is a strong positive linear relationship between the two variables in question.
