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A quick intro here:
The following link is a great example how to use Issue Tree methodology on how to connect the research questions and hypotheses of an opportunity.  These hypotheses are structural hypotheses, not statistical hypotheses in the first stage and they are converted into statistical hypotheses. These structural hypotheses have to be evaluated for doability and its value, and the one with maximum expected values are given up to some thresholds are given higher priorities to to utilize in the problem solving. Also, the end nodes, also called data strategies, provide the opportunity to understand the collection of data elements needed at each of those end nodes and help make the request for data on the basis of those collection of data elements.

The analyst /consultant and the client both understand what key end nodes will get used in the analytics based on the expected values of the nodes as well as availability of data.  This eliminates any possibility of surprise as to what is not addressed during the analytics solution development stage.

It provides various hypotheses pathways and different pathways are evaluated on the expected pay off as follows.  This is also from the same reference mentioned above.

A case study going through the tree details is given here on the issue of “how can we increase profit”, and hence the “issue tree” for “increasing profit”.

Now for each of the issue-tree end point, we can calculate

In an example based on more dimensions, look at

Please read all the wonderful details the first link provides.



08/21/2016 2:01am

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02/06/2017 6:51pm

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02/07/2017 8:03pm

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04/03/2017 10:58pm

Great blog post. Keep up the good work.

04/11/2017 8:52am

Thanks for this concise and excellent lesson on issue trees. Well done!

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