Central to the idea of variance components models is the idea of fixed and random effects. Each effect in a variance components
model must be classified as either a fixed or a random effect. Fixed effects arise when the levels of an effect constitute
the entire population in which you are interested. For example, if a plant scientist is comparing the yields of three varieties
of soybeans, then `Variety`

would be a fixed effect, providing that the scientist was concerned about making inferences about only these three varieties
of soybeans. Similarly, if an industrial experiment focused on the effectiveness of two brands of a machine, `Machine`

would be a fixed effect only if the experimenter’s interest did not go beyond the two machine brands.

On the other hand, an effect is classified as a random effect when you want to make inferences about an entire population,
and the levels in your experiment represent only a sample from that population. Psychologists comparing test results between
different groups of subjects would consider `Subject`

as a random effect. Depending on the psychologists’ particular interest, the `Group`

effect might be either fixed or random. For example, if the groups are based on the sex of the subject, then `Sex`

would be a fixed effect. But if the psychologists are interested in the variability in test scores due to different teachers,
then they might choose a random sample of teachers as being representative of the total population of teachers, and `Teacher`

would be a random effect. Note that, in the soybean example presented earlier, if the scientists are interested in making
inferences about the entire population of soybean varieties and randomly choose three varieties for testing, then `Variety`

would be a random effect.

If all the effects in a model (except for the intercept) are considered random effects, then the model is called a *random-effects model*;
likewise, a model with only fixed effects is called a *fixed-effects model*. The more common case, where some factors are fixed and others are random, is called a *mixed model*.
In PROC VARCOMP, by default, effects are assumed to be random. You specify which effects are fixed by using the FIXED= option in the MODEL statement. In general, if an interaction or nested effect contains any effect that is random, then the interaction or nested
effect should be considered a random effect as well.

In the linear model, each level of a fixed effect contributes a fixed amount to the expected value of the dependent variable.
What makes a random effect different is that each level of a random effect contributes an amount that is viewed as a sample
from a population of normally distributed variables, each with mean 0, and an unknown variance, much like the usual random
error term that is a part of all linear models. The estimate of the variance associated with the random effect is known as
the *variance component*
because it measures the part of the overall variance contributed by that effect. Thus, PROC VARCOMP estimates the variance
of the random variables that are associated with the random effects in your model, and the variance components tell you how
much each of the random factors contributes to the overall variability in the dependent variable.