Interpreting Multiple Linear Regression Summary
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
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