[R] Interpreting Multiple Linear Regression Summary

[B77S]

This is the output of dput(your data)


structure(list(Ca = c(NA, NA, 24.4, NA, 21.4, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28, 32, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, 34.7, NA, 42.5, NA, 26, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.6, 21.4, NA, 48.3, 
63.5, NA, NA, 28.7, NA, NA, NA, NA, 64.3, 23), Cl = c(1.58, 5.6, 
3, NA, 1, 5, 1.2, 4, 4, 8.4, 1, 1.4, 4.9, 1.7, 2, 1.6, 3.3, 2.2, 
9, 1, 2, 1, 1, 5, 4, 3, 2.27, 1.76, 5.81, 4.23, 4.23, 6.25, 6.72, 
4, NA, 5, 5.8, 5.8, 2.2, 5.4, 5.4, 4.8, 8, 1, 4.8, 5.9, 5.9, 
13, 5.6, 1.2, NA, NA, NA, 3, 7, NA, NA, 2, NA, NA, NA, NA, 7, 
4.1), Cond = c(NA, NA, 190, 187, 184, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 248, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 304, 354, 379, NA, 300, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.2, 187, 285, 378, 533, 
207, 262, 244, 238, 280, 380, 402, 636, 300), Mg = c(NA, NA, 
10, NA, 9.1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 11, 12, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
17.4, NA, 21.1, NA, 24, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, 9.5, NA, 22.1, 29.9, NA, NA, 12.6, NA, NA, NA, NA, 
32.4, 21), Na = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4L, 4L, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA), SO4 = c(9.4, 6.5, 9, NA, 7, 55, 6.8, 105, 
15.6, 8.4, 8.8, 19.4, 37, 12, 10, 9.1, 34, 11, 69, 18, 9, 13, 
9, 7, 6, 5, 7.8, 7.8, 7.5, 6, 7.3, 7, 7.5, 6, 7, 7, 5.6, 5.6, 
5.4, 11, 10.5, 9.9, 11.7, 8.4, 12.1, 16, 20, 7.6, 17, 6.5, NA, 
8, 22, 24, 44, NA, 13, 13, 12, 18, 23, 23, 73, 4), TDS = c(105L, 
181L, 112L, 144L, 114L, 308L, 96L, 430L, 108L, 108L, 125L, 129L, 
360L, 140L, 95L, 120L, 280L, 130L, 352L, 148L, 107L, 125L, 139L, 
188L, 201L, 178L, 197L, 187L, 182L, 165L, 186L, 191L, 190L, 176L, 
175L, 220L, 163L, 163L, 152L, 221L, 171L, 204L, 174L, 190L, 174L, 
210L, 190L, 180L, 200L, 180L, NA, 120L, 135L, 228L, 14L, NA, 
156L, 140L, 128L, 160L, 215L, 230L, 316L, 163L)), .Names = c("Ca", 
"Cl", "Cond", "Mg", "Na", "SO4", "TDS"), class = "data.frame", row.names =
c(NA, 
-64L))



B77S wrote:
> 
> Please see ?dput
> 
> use dput(your data) and paste the output into a reply, thanks.
> 
> This way we know what you are working with. 
> 
> 
> 
> 
> 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))
>> 
>> 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
>> 
>> ______________________________________________
>> R-help@ mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>> 
> 


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