Positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and **strong positive correlation from 0.5 to 1.0**.

Correlation coefficients whose magnitude are between 0.5 and 0.7 indicate variables which can be considered moderately correlated. Correlation coefficients whose magnitude are between 0.3 and 0.5 indicate variables which have a low correlation.

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.

For example, with demographic data, we we generally consider correlations above 0.75 to be relatively strong; correlations between 0.45 and 0.75 are moderate, and those below 0.45 are considered weak.

The relationship between two variables is generally considered strong when their r value is larger than 0.7. The correlation r measures the strength of the linear relationship between two quantitative variables. Pearson r: • r is always a number between -1 and 1.

A weak positive correlation indicates that, although both variables tend to go up in response to one another, the relationship is not very strong. A strong negative correlation, on the other hand, indicates a strong connection between the two variables, but that one goes up whenever the other one goes down.

Positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0.

Conclusion. In summary: As a rule of thumb, a correlation greater than 0.75 is considered to be a “strong” correlation between two variables.

The strength of the relationship is a relative issue; however, a correlation of 0.52 may not be considered a large one. The practical significance of the relationship should be considered when we think about the strength of the correlation/ relationship. Your correlation level is not large; it is around moderate level.

r = 0.20 – 0.39 is considered a weak relationship. r = 0.40 – 0.59 is considered a moderate relationship. r = 0.60 – 0.79 is considered a strong relationship. r = 0.80 – 1 is considered a very strong relationship.

As a rule of thumb, a correlation coefficient between 0.25 and 0.5 is considered to be a “weak” correlation between two variables.

It is also quite capricious to claim that a correlation coefficient of 0.39 represents a “weak” association, whereas 0.40 is a “moderate” association.

High degree: If the coefficient value lies between ± 0.50 and ± 1, then it is said to be a strong correlation. Moderate degree: If the value lies between ± 0.30 and ± 0.49, then it is said to be a medium correlation. Low degree: When the value lies below + . 29, then it is said to be a small correlation.

In our case, it represents the probability that the correlation between x and y in the sample data occurred by chance. A p-value of 0.05 means that there is only 5% chance that results from your sample occurred due to chance.

If the correlation coefficient is greater than zero, it is a positive relationship. Conversely, if the value is less than zero, it is a negative relationship. A value of zero indicates that there is no relationship between the two variables.

For example, a correlation coefficient of 0.2 is considered to be negligible correlation while a correlation coefficient of 0.3 is considered as low positive correlation (Table 1), so it would be important to use the most appropriate one.

A negative correlation indicates two variables that tend to move in opposite directions. A correlation coefficient of -0.8 or lower indicates a strong negative relationship, while a coefficient of -0.3 or lower indicates a very weak one.

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 ...

The value for the Pearson correlation coefficient given here is stated to be equal to 0.53. This means that the relationship between the variables which were analyzed is both positive and moderate in magnitude.

The cutoff for defining a large correlation is 0.5. Strongly related variables may have a correlation close to zero if the relationship is nonlinear. If the standard deviations of X and Y are 15.5 and 10.8, respectively, and the covariance of X and Y is 128.8, then the correlation coefficient is approximately 0.77.

The magnitude of the correlation coefficient indicates the strength of the association. For example, a correlation of r = 0.9 suggests a strong, positive association between two variables, whereas a correlation of r = -0.2 suggest a weak, negative association.

If is between 0.1 and 0.5, there is a weak correlation. If is less than 0.1, there is no apparent correlation.

A coefficient of correlation of +0.8 or -0.8 indicates a strong correlation between the independent variable and the dependent variable. An r of +0.20 or -0.20 indicates a weak correlation between the variables. When the coefficient of correlation is 0.00 there is no correlation.

In general, -1.0 to -0.70 suggests a strong negative correlation, -0.50 a moderate negative relationship, and -0.30 a weak correlation.

Since the coefficient (0.41) is positive, it indicates the presence of a positive correlation. The value of 0.41 is between 0.3 to 0.7 (range for moderate correlation), indicating the correlation strength to be moderate.