Helps any serious data analyst with a computer to recognize the strengths and limitations of data, to test the assumptions implicit in the least squares methods used to fit the data, to select appropriate forms of the variables, to judge which combinations of variables are most influential, and to state the conditions under which the fitted equations are applicable. This edition includes numerous extensions and new devices such as component and component-plus-residual plots, cross verification with a second sample, and an index of required x-precision; also, the search for better subset equations is enlarged to cover 262,144 alternatives. The methods described have been applied in agricultural, environmental, management, marketing, medical, physical, and social sciences. Mathematics is kept to the level of college algebra.
|Series:||Wiley Classics Library Series , #75|
|Product dimensions:||6.32(w) x 9.29(h) x 0.86(d)|
Table of Contents
Assumptions and Methods of Fitting Equations.
One Independent Variable.
Two or More Independent Variables.
Fitting an Equation in Three Independent Variables.
Selection of Independent Variables.
Some Consequences of the Disposition of the Data Points.
Selection of Variables in Nested Data.
Nonlinear Least Squares, a Complex Example.