  Statisticians generally use ordinary least squares to minimize the random
error in a subject response with respect to independent explanatory variable.
However, Wooten shows illustrates how ordinary least squares can be used to
minimize the random error in the system without defining a subject response.
Using lattice design Wooten shows that non-response analysis is a superior
alternative rotation of the pyramidal relationship between random variables and
parameter estimates in multi-linear regression. Non-Response Analysis for
simple linear co-linearity and Rotational Analysis in Simple Linear Regression
challenge the notion of fixed effects; unity is included as a random measure
(variable). The illustrations using lattice designs a mean operator that
generates the standard mean and the self-weighing mean, among other point
estimates with random weights; and a join that illustrates variance and
covariance; and develops the measures of variance referred to as internal
co-variance and base variance. These concepts are used to illustrate how these
measures are used to evaluate parameter estimates in standard simple linear
regression and simple implicit regression (non-response and rotational). The
resulting analysis of these lattice designs show standard simple linear
regression limits the relationship by consider the variance in one direction as
relating to the two adjacent co-variances (standard and internal) whereas
non-response analysis defines the relationship in terms of the internal
co-variances and the base variance.
