# Regression Analysis Help

#### MohammedT

##### New Member
Hi All, first post here, glad to be part of this forum.

After surveying my population, I am currently experimenting what factors (about 10 explanatory variables) are influencing a dependent variable, the data is as follows:

Dependent variable is Discrete, it is ordinal, and has 5 values.

Explanatory variables (10) are Discrete, ordinal, and have 5 values.

Questions: is Logistic Regression Analysis suitable for the above data? I want to reveal the relationship between the dependent variable and several explanatory variables.

I will be using Minitab.

Thanks
Mohammed

#### WeeG

##### TS Contributor
Ordinal Logistic Regression is the model you are looking for.

But, I would take it carefully if I were you. Your independent variables are ordinal, and have 5 values each, which is 4 dummy variables for each one, 10 times, it's like having 40 variables. If your ordinal variables come from a survey, check if they are correlated, if so, try to use principal components, and use the new components as your new independent variables.

Minitab support all that.

#### MohammedT

##### New Member
Ordinal Logistic Regression is the model you are looking for.

But, I would take it carefully if I were you. Your independent variables are ordinal, and have 5 values each, which is 4 dummy variables for each one, 10 times, it's like having 40 variables. If your ordinal variables come from a survey, check if they are correlated, if so, try to use principal components, and use the new components as your new independent variables.

Minitab support all that.
Hi WeeG

How do I check if they are correlated, and what is "principal components".

a bit new to all this, thanks for your help on this matter.

Regards,
Mohammed

#### WeeG

##### TS Contributor
examine the correlation between all pairs of independent variables. If you see some suspicious correlations, run a linear regression model (not logistic) with ANY dependent variable (if needed, create one) and calculate the VIF for each independent variable. If you find VIF values of 5 or more (some say 3 or more), your variables are correlated.

Principle components analysis is a method of taking p correlated independent variables, and creating k (<<p) independent variables that are not more than linear combinations of the original variables. These components can then be entered as "new" independent variables to a regression model.

if your IV's are representing the same thing, maybe you can build some summarizing measure from them (like a mean of them all)

#### MohammedT

##### New Member
Thanks for your help WeeG, will follow this above process.

Greatly appreciated.