# mean+standard deviation+skewness+kurtosis

#### WEIBO

##### New Member
hi

I want to order some variables according to (mean+standard deviation+skewness+kurtosis) or some new rule considering mean value, standard deviation, skewness and kurtosis simultaneously. Is there any similar sorting rule like this? Is this equation used in other research ares?

thank you.

#### Dason

It's not really clear to me what you want to do. I think the more important question though is what you're *actually* trying to do. What is it that you want to order the variables by and why?

#### WEIBO

##### New Member
It's not really clear to me what you want to do. I think the more important question though is what you're *actually* trying to do. What is it that you want to order the variables by and why?

The variables are data sets which include several different numbers. I want to order these sets according to their mean, standard deviation, skewness and kurtosis. Is there any similar method used for some research area? or how to determine the weights of each parameter, such as a*mean+b*std+c*ske+d*kur?

thank you

#### Dason

That didn't help at all. WHY do you want to do this. What is your actual purpose here. I'm assuming you're not just ordering them and then saying "Good job me! Now I'm all done". Why do they need to be ordered. What is your actual goal.

#### hlsmith

##### Less is more. Stay pure. Stay poor.
WEIBO objective is to burden Dasonbot processing power. Objective achieved. Repeat objective indefinitely until objective failure, then engage machine learning and execute new burdening actions.

#### WEIBO

##### New Member
That didn't help at all. WHY do you want to do this. What is your actual purpose here. I'm assuming you're not just ordering them and then saying "Good job me! Now I'm all done". Why do they need to be ordered. What is your actual goal.
The goal is to order them according to their mean value and variation. That is the set with large mean value and large variation is ordered first.

#### Dason

I'm done. $$\hspace{1in}$$