Advisor 2 a Study of Industrial Marketing Budgeting Part 2 Change Models

Gary Louis Lilien

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136 * CPLANS (2. 1) -1. 128 * PMSD (2. 7) + . 098 * LN(USERS 2) (2. 9) - . 124 * LN(SLMEN) (1. 3) - . 497 * CONC ( . 7) - . 151 * LFCYC ( -6) - . 221 * ADVDUM ( . 7) EXHIBIT 6 Personal Advertising Change Model LOGIT(PERS) = PLOGIT R = , 22 SE = 1.
, 00 F ■ 2.
63 N = 44 -2. 608 + . 582 * LFCYC (1. 8) -1. 875 * PMSD (1. 8) + . 170 * LN(SLMEN) (1. 7) +1. 082 * NCOM (1. 3) -13- EXHIBIT 7 Change Model Comparison PERSP ADVERP IMPERSP MKTP LFCYC PMSD LSLMEN NCOM CPLANS LUSERS CONC ADVDUM + - + + - + +... + - - - - - + + - - - + - - -14- Several issues can be raised however. The models are simple and do not explain an overwhelming portion of data variance. Several more complex model forms were tried (uith various variable transformations) with no significant change in fit. Scattergrams do not reveal patterns of non- linearity. We conclude: 1. Our task is difficult as, in a sense, we try to explain residual variation (what's left over, on a year-to-year basis, from the norm models). Thus we cannot expect our models to have as high explanatory power as the norm models.

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