[R] COnfidence intervals for estimates of linear model

Greg Snow Greg.Snow at imail.org
Wed Dec 23 17:59:18 CET 2009


Using 1.95996 is appropriate when you know the population standard deviation or the sample size is approximately infinity.  Otherwise you should use the t-distribution, the qt function is useful for that.

Or if you want intervals for the coefficients look at the confint function, if you want intervals for predictions, then look at predict.lm as has been pointed out (estimates could be interpreted either way).

Hope this helps,

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Daniel Brewer
> Sent: Wednesday, December 23, 2009 3:28 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] COnfidence intervals for estimates of linear model
> 
> Hello,
> 
> I would like to calculate the 95% confidence intervals for the
> estimates
> of a linear model and I just wanted to check that I am doing it
> correct.
>  Is it just:
> 
> Estimate + 1.95996*Std.Error to Estimate - 1.95996*Std.Error
> 
> or is there another approach that doesn't assume a normal distrbution?
> 
> Thanks.  Apologies for my naiivity
> 
> Dan
> 
> --
> **************************************************************
> Daniel Brewer, Ph.D.
> 
> Institute of Cancer Research
> Molecular Carcinogenesis
> Email: daniel.brewer at icr.ac.uk
> **************************************************************
> 
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