MLR  Analysis of Variance
n ... number of observations
k ... number of independent variables
The ANOVA table above shows the calculations for a multiple regression
model with k independent variables and n observations. The following remarks
give some hints on how to interpret the ANOVA table:
 For k = 1 the table above is reduced to simple linear regression
 The Fratio tests the hypothesis that all coefficients a_{0} ..
a_{n} of the independents variables are zero (null hypothesis).
The Fratio is distributed according to an F distribution with k and nk1
degrees of freedom. Also, the F value is related to the goodness of fit,
r^{2}, through the following equation:
 The residual sum of squares SS_{res} is an estimate of the variability
along the regression line. SS_{res} can be used to find the estimated
standard errors of the individual regression coefficients a_{i}.
The estimated standard error follows a tdistribution with nk1 degrees
of freedom. The confidence interval for the individual coefficients is
given by +/ t(α/2, nk1)s(a_{i}).
 If two variables x_{i}, and x_{j}, are highly correlated,
the regression coefficients are difficult to estimate, and their actual
numeric values probably do not reflect real dependencies.
