It is a measure of the strength of a linear relationship between one dependent variable and one or more explanatory variables.
It is very important to recognise that a high level of correlation does not imply causation. Also, it does not provide information on non-linear relationships.
True.
true
If two variables are highly correlated, the Pearson correlation will be close to -1.0 or +1.0. A correlation of zero shows no relationship.
event B has something in common with event A
event B has something in common with event A
true
The Matthew correlation coefficient considers true and false positives and negatives. The specificity correlation only considers the true classes or rejections.
If measurements are taken for two (or more) variable for a sample , then the correlation between the variables are the sample correlation. If the sample is representative then the sample correlation will be a good estimate of the true population correlation.
No, that is not a causative correlation.
No, that is not a causative correlation.
True.
true
True
If two variables are highly correlated, the Pearson correlation will be close to -1.0 or +1.0. A correlation of zero shows no relationship.
A correlation interval refers to the range within which the correlation coefficient, a statistical measure of the strength and direction of a relationship between two variables, is assessed. Typically, this interval ranges from -1 to +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 denotes no correlation. In practice, correlation intervals can also refer to confidence intervals around the correlation coefficient, providing a range of values that likely includes the true correlation in the population.
By multiple observations it was noted that that correlation was true.
The effect occurs before the cause.