The function `summary.lm`

computes and returns a list of summary
statistics of the fitted linear model given in `object`

, using
the components (list elements) `"call"`

and `"terms"`

from its argument, plus

residualsthe *weighted* residuals, the usual residuals
rescaled by the square root of the weights specified in the call to
`lm`

.

coefficientsa \(p \times 4\) matrix with columns for
the estimated coefficient, its standard error, t-statistic and
corresponding (two-sided) p-value. Aliased coefficients are omitted.

aliasednamed logical vector showing if the original
coefficients are aliased.

sigmathe square root of the estimated variance of the random
error
$$\hat\sigma^2 = \frac{1}{n-p}\sum_i{w_i R_i^2},$$
where \(R_i\) is the \(i\)-th residual, `residuals[i]`

.

dfdegrees of freedom, a 3-vector \((p, n-p, p*)\), the first
being the number of non-aliased coefficients, the last being the total
number of coefficients.

fstatistic(for models including non-intercept terms)
a 3-vector with the value of the F-statistic with
its numerator and denominator degrees of freedom.

r.squared\(R^2\), the ‘fraction of variance explained by
the model’,
$$R^2 = 1 - \frac{\sum_i{R_i^2}}{\sum_i(y_i- y^*)^2},$$
where \(y^*\) is the mean of \(y_i\) if there is an
intercept and zero otherwise.

adj.r.squaredthe above \(R^2\) statistic
‘*adjusted*’, penalizing for higher \(p\).

cov.unscaleda \(p \times p\) matrix of (unscaled)
covariances of the \(\hat\beta_j\), \(j=1, \dots, p\).

correlationthe correlation matrix corresponding to the above
`cov.unscaled`

, if `correlation = TRUE`

is specified.

symbolic.cor(only if `correlation`

is true.) The value
of the argument `symbolic.cor`

.

na.actionfrom `object`

, if present there.