The
linear regression model computes several assessment measures to help you evaluate how well the model
fits the data. These assessment measures are available at the top of the model pane.
Click the currently displayed assessment measure to see all of the available assessment
measures.
Adjusted R-square
The Adjusted R-squared
value attempts to account for the addition of more effect variables.
Values can range from 0 to 1. Values closer to 1 are preferred.
AIC
Akaike’s Information Criterion. Smaller values indicate better models, and
AIC values can become negative. AIC is based on the Kullback-Leibler information measure
of discrepancy between the true distribution of the
response variable and the distribution specified by the model.
AICC
Corrected Akaike’s Information Criterion. This version of AIC adjusts the value to
account for
sample size. The result is that extra effects penalize AICC more than AIC. As the sample size
increases, AICC and AIC converge.
F Value for Model
The value of the F
test in a one-way ANOVA after the variances are normalized by the
degrees of freedom. Larger values are better, but can indicate overfitting.
Mean Square Error
The mean squared error
(MSE) is the SSE divided by the degrees of freedom for error. The
degrees of freedom for error is the number of cases minus the number
of weights in the model. This process yields an unbiased estimate
of the population noise variance under the usual assumptions. Smaller
values are preferred.
Observations
The number of observations used in the model.
Pr > F
The p-value
associated with the corresponding F statistic. Smaller values are
preferred.
R-Square
The R-squared value
is an indicator of how well the model fits the data. R-squared values
can range from 0 to 1. Values closer to 1 are preferred.
SBC
The Schwarz’s Bayesian Criterion (SBC), also known as the Bayesian Information Criterion
(BIC), is an increasing function of the model’s residual
sum of squares and the number of effects. Unexplained variations in the response variable and the
number of effects increase the value of the SBC. As a result, a lower SBC
implies either fewer
explanatory variables, better fit, or both. SBC penalizes free
parameters more strongly than AIC.