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11692Re: statistics books

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  • nevertrustab
    Jul 7, 2007
    • 0 Attachment
      Writing my own book lol..you're about the 5th person that has told
      me this during the last 12 months. The other day some woman who
      needed help with her (altruistic) website asked a question and I
      answered by breaking down all the basics on how to get started with
      SEO in a >2 pages long post to help her..afterwards I was adviced I
      might want to break down my posts into chapters LOL (ironically of
      course).

      Maybe I will get to do that later hehe.

      --- In webanalytics@yahoogroups.com, "Dave and Kathleen Barber"
      <barbers@...> wrote:
      >
      > Janet, I got the 1st book you listed...Jim is great, but your
      marketing is
      > better. Patriccc, that's the longest post I've seen in a while.
      Prehapse
      > you should write your own book.
      >
      > On 6/29/07, nevertrustab <patriccc@...> wrote:
      > >
      > > Hi Janet,
      > >
      > > those books sound interesting. Actually data mining is what I had
      > > wanted to get into before I "found out" about the web :-).
      > >
      > > I read lots of stuff about data mining and actually I've asked
      > > myself this question before, too:
      > >
      > > Everybody talks about it and all...but is it really effective?
      > >
      > > From what I read in data mining theres a lot of the typical tech-
      guy
      > > doesnt understand business guy. business guy thinks tech guy is
      not
      > > necessary - thing going on lol. (probably not the only
      > > interdisciplinary field which has that problem).
      > >
      > > To be honest, I still don't know how effective "data mining"
      really
      > > is and whether it's simply hyped up (I mean it IS sort of a cool
      > > buzz-word that aims to make the dry-sounding "statistics" more
      > > exciting...when statistics is the main part of it (at least Ive
      been
      > > told so)).
      > >
      > > But I would argue, that just because of one example where it was
      a
      > > waste of resources that doesn't mean it is generally a waste of
      > > resources. For example..surprise surprise...most of the time
      when I
      > > read about "data mining" they cited mostly (exclusively?) cases
      with
      > > extremely positive results.
      > >
      > > Chances are if we took a (statistically significant ;)) sample of
      > > cases and looked how many times it worked and how many times it
      > > didn't work we'll find many cases where it worked wonders and
      many
      > > cases where it didn't work at all. (I dont dare make an
      assumption
      > > which cases are more frequent/if the overall situation has a
      plus or
      > > a minus).
      > >
      > > However, I would guess that there are quite a few (a lot?) of
      > > companies for which analytical CRM works out well as it seems to
      > > have become quite an established field. Actually I read a couple
      of
      > > days ago that "data mining had its biggest success in business in
      > > the field of CRM" - whether that is a good thing or not..I cant
      > > answer that ;) but it sounds like it does work for some companies
      > > out there.
      > >
      > > So all in all..I guess it's sort of like saying web analytics
      work
      > > (dont work), SEO works (doesnt work). We can't really make a
      > > statement whether something is effective or not unless we have a
      big
      > > enough sample of it as there'll always be cases where it works
      and
      > > where it doesnt. The matter is just in how many of those cases
      does
      > > it work/ doesnt it work? And how big are the benefits if it does
      > > work vs. the losses when it doesn't work?
      > >
      > > I wrote more than I thought I would.. once again ;) but Im sure
      you
      > > know that we shouldnt make a conclusion for a whole field based
      on
      > > one case..I just meant to point this out for data mining as there
      > > seems to be a lot of talk about that.
      > >
      > > --- In webanalytics@yahoogroups.com <webanalytics%
      40yahoogroups.com>,
      > > "Janet Park" <jparkmfi@>
      > > wrote:
      > > >
      > > >
      > > > The following books go beyond mere statistics into the heavy
      > > lifting of
      > > > Data Mining and data modeling. Take these to the beach with you
      > > this
      > > > summer and no quantitative bullies from Cal Tech will kick
      sand in
      > > your
      > > > face!
      > > >
      > > > Mastering Data Mining: The Art and Science of Customer
      Relationship
      > > > Management, by Michael J.A. Berry and Gordon Linoff, John
      Wiley &
      > > Sons,
      > > > 2000.
      > > >
      > > > Data Mining Techniques For Marketing, Sales, and Customer
      Support,
      > > by
      > > > Michael J.A. Berry and Gordon Linoff, John Wiley & Sons, 1997.
      > > >
      > > > Both books offer slightly different twists on the subject and
      I'd
      > > > recommend the pair, even though there is some redundancy. The
      > > authors
      > > > are practicing Data Mining Consultants and their real world
      > > experience
      > > > shows. They not only tell "how to," but even more
      important, "how
      > > not
      > > > to." Here's a bold example from Mastering Data Mining:
      > > >
      > > > "Is the Data Mining Effort Necessary?
      > > >
      > > > A Senior Vice President in the credit card group of a large
      bank
      > > has
      > > > spent tens of thousands of dollars developing a response model.
      > > This
      > > > predictive model is designed to identify the porpsects who are
      most
      > > > likely to respond to the bank's next offering. The VP is told
      that
      > > by
      > > > using the model, she can save money; using only 20 percent of
      the
      > > > prospect list will yield 70 percent of the responders. However,
      > > despite
      > > > these findings, she replies that she wants every single
      responder -
      > > - not
      > > > just some of them. Getting every responder requires using the
      > > entire
      > > > prospect list, since no model is perfect. In this case, data
      > > mining is
      > > > not necessary.
      > > >
      > > > Moral: She could have saved tens of thousands of dollars by not
      > > building
      > > > predictive models in the first place.
      > > >
      > > > I keep both handy as references and learn something each time I
      > > pick
      > > > them up, even though I've been practicing in this field for
      over 15
      > > > years. You don't need to be a statistical guru to understand
      them -
      > > -
      > > > just skip over the hairy details if you prefer an "executive's"
      > > approach
      > > > to the subject.
      > > >
      > > >
      > > >
      > > > [Non-text portions of this message have been removed]
      > > >
      > >
      > >
      > >
      >
      >
      > [Non-text portions of this message have been removed]
      >
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