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Statistics
Tom Schori is our Millennium Marketing Research® statistics guy. His background in statistics is what might be called heavy duty. In the area of multivariate statistics, he has developed and published theretofore never described statistical procedures, viz., Multiple Discriminant Analysis: A Repeated Measures Design and Versatile MANOVA (Multivariate Analysis of Variance with repeated measures). Here are the citations:
In the early days of his career, he often was invited to give lectures on multivariate statistical topics to faculty and graduate students at the Medical College of Virginia, Biometrics Department (Statistics), something which he considered a real honor. Over the years, he has stayed at the leading edge of statistics, as applied to the world of marketing, as these citations would suggest:
Furthermore, his long term interest remains in developing innovative new statistical procedures that can be utilized in marketing environments, such as the Optimal Brand Positioning Model© which he described in the following professional journal article:
The two Millennium Marketing Research® principals, Tom Schori & Mike Garee, together, have continued to be interested in a wide variety of statistical topics:
We say all this for one reason. To demonstrate that we have considerable knowledge and experience with statistics. That's true with both univariate and multivariate statistics. With us, it is not a question of using some statistics program to calculate some statistic or another. We know the statistics inside and out, when they should be used, and when not. But the fact of the matter is that, for most marketing research studies we conduct, nothing more sophisticated is needed than well thought-out cross-tabs, with careful consideration given to what constitutes both the "banner" and "stub" variables. To summarize all these many words, we'd do whatever statistical procedure is appropriate. But most often, crosstabs are really all that are needed for us to thoroughly analyze an effort's data, draw meaningful conclusions about the data, and then develop readily implementable recommendations. I |