Machine Learning - Data Description - Coefficient of Variation

In this video we shall discuss on coefficient of variation

The coefficient of variation denoted by c vhar is the standard division divided by the mean

The result is expressed as a percentage. So here you see if we calculate this c var for the samples in that case it will be standard

division will be expressed in the form of s and for the sample the respective mean will

be x-base in form of x-bar. So, s by x-bar into 100%. But if we do the same calculation

in case of population, then c-vard is equal to sigma, that is a, that is a symbol for the

standard division for population and mu, that is the mean for the population, into 100%. So

in this way, you are getting this c-vart, that is, s by x-bar into 100%, and in case of population

cifur is equal to sigma by mu into 100 Now let us go for one example for the better understanding So sales of automobiles The mean of the number of sales of car over a three months period is 87

So, mean is 87 and the standard division is 5. So that is for the sales of the car

The mean is 87 and standard division is 5. But on the other end, the mean of the commission is $52 to $5 and the standard division is $773

So, compare the variations of the two. So, now it is quite obvious that we'll be calculating the c-var and S by x-bar

So putting this value, I'm getting this 5.7 percentage. And in case of c-war, in case of commission, we're having this respective values we

have put from the problem and 14.8% is the respective commissions. Since the coefficient of variation is larger for commissions, the commissions are more variable

than the cells. So, you can find this commission is more variable compared to the cells

So in this way we have defined that what is a coefficient of variation

Thanks for watching this video