1. • Alternative terminology in statistics
• In statistics, the dependent/independent variable terminology is used more widely
than just in relation to controlled experiments. For example the data analysis of
two jointly varying quantities may involve treating each in turn as the dependent
variable and the other as the independent variable. However, for general
usage, the pair response variable and explanatory variable is preferable as
quantities treated as "independent variables" are rarely statistically
independent.[2][3]
• Depending on the context, an independent variable is also known as a "predictor
variable," "regressor," "controlled variable," "manipulated variable," "explanatory
variable," "exposure variable," and/or "input variable."[4] A dependent variable is
also known as a "response variable," "regressand," "measured variable,"
"observed variable," "responding variable," "explained variable," "outcome
variable," "experimental variable," and/or "output variable."[5]
• In addition, some special types of statistical analysis use terminology more
relevant to the specific context. For example reliability theory uses the term
exposure variable for what would otherwise be an explanatory or independent
variable; medical statistics may use the term risk factor; and machine learning and
pattern recognition use the term feature.

2. • In statistics, a full factorial experiment is an experiment whose
design consists of two or more factors, each with discrete possible
values or "levels", and whose experimental units take on all
possible combinations of these levels across all such factors. A full
factorial design may also be called a fully crossed design. Such an
experiment allows studying the effect of each factor on the
response variable, as well as the effects of interactions between
factors on the response variable.
• For the vast majority of factorial experiments, each factor has only
two levels. For example, with two factors each taking two levels, a
factorial experiment would have four treatment combinations in
total, and is usually called a 2×2 factorial design.
• If the number of combinations in a full factorial design is too high to
be logistically feasible, a fractional factorial design may be done, in
which some of the possible combinations (usually at least half) are
omitted.