Re: CLT and regression
- From: "Anon." <bob.ohara@xxxxxxxxxxxxxxxxx>
- Date: Wed, 19 Apr 2006 08:00:56 +0300
r.c.reulen@xxxxxxxxx wrote:
Hello,To follow up Ray's post, you should fit the regression (1000 subjects isn't that large!), and look at the residuals for normality (e.g. with normal probability plots). They may be normal, in which case you're fine. If they're not, e.g. if they're skewed, then you could look at using a Box-Cox transformation to get them normal: i.e. you use a power transformation (y^alpha, and log(alpha) if alpha=0 is indicated). Generally you don't have to be too precise with the transformation: for positively skewed residuals, trying square root, cube root and log transformations often gets you close enough to normality.
Can someone explain how the central limit theorem is related to
regression analysis? I understand the basics of the CLT, but don't
understand its relationship with regression analyis. I am conducting
an analysis with approx. 1000 subjects. These subjects have a score
between 0-100 on a certain physical functioning (PF) scale. PF is the
dependent variable in my analysis. The distribution of these scores are
highly skewed. Can I still use linear regression analysis? Or should I
go for non-parametric or bootstrapping techniques?
HTH
Bob
--
Bob O'Hara
Department of Mathematics and Statistics
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Telephone: +358-9-191 51479
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WWW: http://www.RNI.Helsinki.FI/~boh/
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