Correlation, Covariance and Causation

A measure used to represent how strongly two random variables are related known as "Correlation" 

"Covariance" is nothing but a measure of correlation 

 Correlation refers to the scaled form of covariance. Correlation is dimensionless,i.e, it is a   unit free measure of the relationship between variables Covariance indicates how two variables are related.                         
A positive covariance means the variables are positively related, while a negative covariance means the variables are inversely related.

To understand covariance clearly we should know what is difference covariance and variance

"Variance" refers to the spread of the data set --how far apart the numbers are in relation to the mean 

"Covariance" refers to the measure of how two random variables will change together and are used to calculate the correlation between variables.

So, we can clearly understand by above  that to find the correlation between two variables we use covariance i.e., covariance is a measure to find the correlation

 the formula for finding covariance,

The "Pearson correlation coefficient" is a very helpful statistical formula that measures the strength between variables and relationships 

pearson correlation coefficient 

The pearson correlation coefficient can take a range of values from +1 to -1 

A value of '0' indicates that there is no association between the two variables

A value greater than '0' indicates a positive association 

A value less than '0' indicates a negative association 

For example -0.97 is a strong negative correlation while a correlation of 0.10 would be a weak positive correlation.

"Causality" indicates a relationship between two events where one event is affected by the other, when the value of one event,, or variable, increases or decreases as a result of other events, it is said there is causation 

"Correlation does not imply causation"