Programming the statistical procedures from SAS

model building

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model building

I have a list of variables and built a regression model on them. The R-square is 0.1. Are there any ways to improve the predictive power of the model without finding more variables?

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Re: model building

At r-square = 0.1, your model has essentially no predictive power. If you give some description of the variables and of the way the observations were acquired (sampling/experimental plan) someone might be able to suggest a better statistical model or at least, alternative data exploration methods and modeling approaches.

PG

PG
Contributor
Posts: 33

Re: model building

"At r-square = 0.1, your model has essentially no predictive power."

Are you sure? The CMS-HCC model used by CMS to pay private insurance companies has R-square of 0.09. The model my company is using has R-square of 0.1, but it already helped the company save a lot of money.

Variable transformation is one way which possible help improve the model's predictive power. I would like to know what other options I can have without acquiring more variables.

Respected Advisor
Posts: 4,742

Re: model building

It is true that strength of evidence requirements vary a lot across disciplines.  -  To go beyond regression, you might want to consider the model building techniques provided by data mining tools - PG

PG
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