An important problem in applied statistics is fitting a given model function f(fJ) with unknown parameters fJ to a data vector y . Minimizing the residual sum of squares provides the least squares estimates of p. If fUi) is linear in fJ the precision of these estimates is well· known. In a nonlinear case approximate (thauah asymptotically exact) confidence statements can be made. BEALE [I] introduced measures of nonlinea rity which can be used to indicate when approximate confidence statements are appropriate. GUTTMAN and MEETER  showed that in some. severely nonlinear. cases Beale's measures do not give the riaht indie-ation. In this paper two new nonlinearity measures are introduced and their use is illustrated on a practical problem described by WITT . A more detailed discussion of the theoretical background can be found in references  and (2).
|Number of pages||7|
|Publication status||Published - 1975|