[R] Interpreting Multiple Linear Regression Summary

[David Winsemius]

On Nov 9, 2011, at 12:04 PM, Rich Shepard wrote:

>  I would appreciate pointers on what I should read to understand this
> output:
>
> summary(lm(TDS ~ Cond + Ca + Cl + Mg + Na + SO4))

I don't see a data= argument specified, so you are telling lm() that  
your workspace has individual vectors by those names in the formula.  
That is not what is implied by hte rest of your message.

>
> Call:
> lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4)
>
> Residuals:
> ALL 1 residuals are 0: no residual degrees of freedom!
>
> Coefficients: (6 not defined because of singularities)
>            Estimate Std. Error t value Pr(>|t|)
> (Intercept)      125         NA      NA       NA
> Cond              NA         NA      NA       NA
> Ca                NA         NA      NA       NA
> Cl                NA         NA      NA       NA
> Mg                NA         NA      NA       NA
> Na                NA         NA      NA       NA
> SO4               NA         NA      NA       NA
>
> Residual standard error: NaN on 0 degrees of freedom
>  (63 observations deleted due to missingness)
>
>  When I look at the summary for the data frame used for this model I  
> do not
> see an excessive number of missing values or indications why there  
> are no
> residual degrees of freedom. The same model applied to 8 other data  
> frames
> did not produce similar results.
>
> Puzzled,
>
> Rich
>
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David Winsemius, MD
West Hartford, CT