# pvalues vs bheta koefisien vs pearson for features importance

#### Ichsan

##### New Member
Hello, I still confuse, the different concepts for feature importances.
For example: I have 6 variabel. 5 variable was predictor, and 1 variable was target (which predicted).
Suppose: X1, X2, X3, X4, X5 and Y

1. Find most important features by do pearson manually (X1 and Y, X2 and Y, X3 and Y, so on). Best pearson value=best importance variable.

2. Find regression. For Example, I get a equation:
b1(X1)+b2(X2)+b3(X3)+b4(X4)+b5(X5)+intercept=Y.
bigger bheta=best importance variable

3. Using OLS (Ordinally Least Squared), I get pvalues from each variable.
pvalues<0.05 => insignificance effect for target variable (Hyphotesis null is accepted).
smaller pvalues = best importance variable

The question is, which method better? How about concept for each method?
Thank you

#### hlsmith

##### Less is more. Stay pure. Stay poor.
2 and 3 may be considered better in that they adjust for the other variables!

what language is koenfisien?

#### Ichsan

##### New Member
2 and 3 may be considered better in that they adjust for the other variables!

what language is koenfisien?
koenfisien?, sorry for typo. the right word is "Coefficient".

Can you explain more detail about different concept of method 2nd and 3rd?