This summary can be extremely helpful if you do not work regularly in statistics or are a new student.

The proofs of these rules can be purchased for a nominal fee from the Order page.

The step by step calculation for correlation coefficient example illustrates how the values are being used in the formula to find the linear correlation between the data sets.

Correlation Coefficient is a vital aspect used in statistics to calculate the strength and direction of the linear relationship or the statistical relationship (correlation) between the two population data sets.

The formula for correlation between two variables is as follows: The covarince is scaled by the product of the two standard devations of the variables.

This measure is called the Pearson correlation which holds true only when the relationship between two variables is linear in nature.

When the covariance is negative it means the exact opposite i.e.

larger values of one variable correspond to smaller values of another variable.

This page contains the basic Rules for the Mean, Variance, Covariance, and Correlation for the expectation of random variables.

And for zero, it would indicate a weak linear relationship between the variables.

Covariance defined In probability theory and statistics, covariance measures the comovement between two variables i.e.

Formally, the If the correlation coefficient is close to 1, it would indicate that the variables are positively linearly related and the scatter plot falls almost along a straight line with positive slope.

For -1, it indicates that the variables are negatively linearly related and the scatter plot almost falls along a straight line with negative slope.

This page contains the basic Rules for the Mean, Variance, Covariance, and Correlation for the expectation of random variables.

And for zero, it would indicate a weak linear relationship between the variables.

Covariance defined In probability theory and statistics, covariance measures the comovement between two variables i.e.

Formally, the If the correlation coefficient is close to 1, it would indicate that the variables are positively linearly related and the scatter plot falls almost along a straight line with positive slope.

For -1, it indicates that the variables are negatively linearly related and the scatter plot almost falls along a straight line with negative slope.

the amount by which the two random variables show movement or change together.