Hi,

Sorry for the naive question, but what exactly does the 'Adjusted R-squared'
coefficient in the summary of linear model adjust for?

Sample code:

> x <- rnorm(15)
> y <- rnorm(15)
> lmr <- lm(y~x)
> summary(lmr)

Call:
lm(formula = y ~ x)

Residuals:
    Min      1Q  Median      3Q     Max
-1.7828 -0.7379 -0.4485  0.7563  2.1570

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.13084    0.28845  -0.454    0.658
x            0.01923    0.25961   0.074    0.942

Residual standard error: 1.106 on 13 degrees of freedom
Multiple R-squared: 0.0004217,    Adjusted R-squared: -0.07647
F-statistic: 0.005485 on 1 and 13 DF,  p-value: 0.942

> cor(x,y)
[1] 0.02053617


- What factors are included in the adjustment?

many thanks!

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