# R: Explanation of a multiple linear regression summary [duplicate]

I am quite new with R and while i am able to perform the basics i am not yet able to understand the output results. For example:

summary(lmodel) generates the following:

Call:
lm(formula = temperature ~ altitude + sea.distance, data = meteodata)

Residuals:
Min       1Q   Median       3Q      Max
-1.40156 -0.46083 -0.04078  0.50961  1.49844

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  19.736000   0.257222  76.728  < 2e-16 ***
altitude     -0.016509   0.004006  -4.121 0.000714 ***
sea.distance  0.531111   0.414166   1.282 0.216927
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7425 on 17 degrees of freedom
Multiple R-squared:  0.864, Adjusted R-squared:  0.848
F-statistic:    54 on 2 and 17 DF,  p-value: 4.317e-08


I know about the ?summary, help(summary) and i am familiar with the basic concepts, but i would kindly ask for some thorough explanation of the summary in regards to my model (residuals, coefficients, F-statistic, Residual standard error, etc.).

It seems that there are several similar answered questions in this forum, however none of them macthes exactly my case.

Any help is welcome. Thanks in advance

## marked as duplicate by gung♦, Stephan Kolassa, whuber♦Mar 24 '14 at 18:05

• The terminology is all standard from any regression text, with the exception of Residual standard error which is the conditional variance. Never did figure out why. – conjugateprior Mar 24 '14 at 11:03
• This question is an obvious duplicate, which you seem to recognize. If previous threads did not provide the information you needed, you should state what you learned from them & what specifically you still need to know. – gung Mar 24 '14 at 16:10