correlation

In general correlation means the relation between two different entities will come under Bi varient analysys . In this blog we will discuss about below item.

1. Co Variance
2. correlation coefficient
3. Coefficient of Variation

Covariance:

         Covariance is a measure of how much two random variables/datasets vary together or When we want to understand the linearity between two variables/datasets we will use covariance

         It’s similar to variance, but where variance tells you how a single variable varies,Covariance tells you how two variables vary together.

Formula for Covariance:

In our below example we collected some random 5 sample information out of 50 students from one of the university about their Reading and writing time.

Lets calculate CoVariance

The result Cov.Sample is 21,155.55 is positive, meaning that the variables are positively related.

correlation coefficient:

 This is more simple form to identify the linearity alternative to co-variance

Always in between -1 to 1, so that it will be very easy to calculate the linearity between two variables

Formula:        

We will calculate the Correlation coefficient based on the covariance which we calculated above example for Covariance.

In the above screenshot we divided Cov. Sample 21,155.55 with multiplication of Standard Deviation of Writing and Reading.

Important points to consider on Correlation coefficient:

1. A correlation coefficient of 1 means that for every positive increase in one variable/dataset, there is a positive increase of a fixed proportion in the other. For example, shoe sizes go up in (almost) perfect correlation with foot length.
2. A correlation coefficient of -1 means that for every positive increase in one variable, there is a negative decrease of a fixed proportion in the other. For example, the amount of gas in a tank decreases in (almost) perfect correlation with speed.
3. Zero means that for every increase, there isn’t a positive or negative increase. The two just aren’t related.

Note:

There are many ways of calculating the correlation between two variables Person R square is mostly used in case of linear Regression we will discuss on how to calculate the R Square here.

Linearity :

Linearity is the property of a mathematical relationship or function which means that it can be graphically represented as a straight line. Examples are the relationship of voltage and current across a resistor (Ohm’s law), or the mass and weight of an object. wiki