turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Stat Procs
- /
- Non contributing variable in multiple linear regre...

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

12-14-2016 08:24 AM

**A non-contributing predictor variable whose P value is more than .50 is added to an existing multiple linear ****regression model. It will increase R2 value. I want to know if the same variable is removed from the regression model what will happen?**

R2 value going down or no change in R2 value or will it affect mean square error.

Take an example we remove non significant variables in backward elimination method in SAS.. does this reduce r2 value if we remove non significant predictor variable?

Sameer

Accepted Solutions

Solution

12-23-2016
01:56 AM

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

12-22-2016 12:56 PM

As @ballardw said, it is data dependent. Suppose it is highly correlated with a linear combination of two or three other variables, but is not a good predictior in and of itself. Removing it could alleviate multicollinearity, which would result in an increase in R^2. If the nonsignificant variables are completely independent from all other predictor variables, then per the calculation of R^2, it should decrease. But in real data, you DON'T KNOW whether this is the case or not.

Blanket statements rarely apply without examining the data.

Steve Denham

All Replies

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

12-14-2016 10:29 AM

Yes, no or maybe. Insufficient information. Very data and procedure dependent.

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

12-14-2016 12:03 PM

In simple wanted to know can removing non significant vaiables reduce R2 or keep it unchanged in regression?

Solution

12-23-2016
01:56 AM

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

12-22-2016 12:56 PM

As @ballardw said, it is data dependent. Suppose it is highly correlated with a linear combination of two or three other variables, but is not a good predictior in and of itself. Removing it could alleviate multicollinearity, which would result in an increase in R^2. If the nonsignificant variables are completely independent from all other predictor variables, then per the calculation of R^2, it should decrease. But in real data, you DON'T KNOW whether this is the case or not.

Blanket statements rarely apply without examining the data.

Steve Denham

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

12-22-2016 02:41 PM

In MLR we see the R2 value increases by the increase in the predictor variables , but are we not interested in Adj R2 in case of multiple regression ?. More over few text speaks about the control variables which can have effect on the coefficients.