The Linear Model and Hypothesis pp 103-116 | Cite as

# Enlarging the Model

## Abstract

Sometimes after a linear model has been fitted it is realized that more explanatory (x) variables need to be added, as in the following examples.

In an industrial experiment in which the response (y) is the yield and the explanatory variables are temperature, pressure, etc., we may wish to determine what values of the *x*-variables are needed to produce a certain yield. However, it may be realized that another variable, say concentration, needs to be incorporated in the regression model. This can be readily done by simply using a standard regression computational package. In this case the added variable is quantitative and is readily added into the original model.

## Keywords

Usual Model Balance Design Covariance Method Miss Data Point Missing Observation## References

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