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All Blog Posts Tagged 'intervals' (4)

Measuring Levels of Alignment

In my most recent blog, I discussed the idea of aligning the supply of services to market demand.  My conceptualization of “alignment” specifically relates to time intervals: i.e. having people at the right place and at the right time - for example, to take advantage of opportunities - is a sign of alignment.  Alignment for me is often about the relationship between capacity and incapacity:  the ability to supply services versus the inability to satisfy the market demand…

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Added by Don Philip Faithful on June 16, 2018 at 6:30am — No Comments

Fugues of Operational Market Alignment

In the “Ecology of Metrics,” I wrote about “alignment” being a type of metric; alignment can measure the extent to which an organization’s supply or capacity is matched against the demands or needs of the market.  For instance, in a call centre, it would be highly desirable to have agents available to respond to calls at “precisely” the same time that clients are making calls.  If alignment is off even by only 15 to 30 seconds, impatient clients might hang up and never call again.  Similarly…

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Added by Don Philip Faithful on June 2, 2018 at 5:00am — No Comments

Probabilistic Forecasting: Learning Uncertainty

The majority of industry and academic numeric predictive projects deal with deterministic or point forecasts of expected values of a random variable given some conditional information. In some cases, these predictions are enough for decision making. However, these predictions don’t say much about the uncertainty of your underlying stochastic process. A common desire of all data scientists is to make predictions for an uncertain future. Clearly then, forecasts should…

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Added by Kostas Hatalis on March 15, 2018 at 12:00pm — No Comments

Detection of Practical Dependency of Variables with Confidence Intervals

This is an article which attempts to detect dependable variables with non-linear method.

I'm going to apply a method for checking variable dependency which was introduced in my previous post. Because the "dependency" I get with this rule is not true dependency as defined in Probability then I will call variables practically dependent at a confidence level…

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Added by Maiia Bakhova on November 2, 2016 at 11:30am — No Comments

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