socliner.blogg.se - Coherence x

#Coherence x how to
#Coherence x series

#Coherence x series

In the FD, the input is a continuous multivariate data matrix $X=$, where each column is a series (e.g., a time-series). The following notation will be used: symbol This leads to refinement of X-Matrix, more coherence and stronger alignment.FourierAnalysis estimates coherence both in the frequency domain, in this unit, and in the time-frequency domain, in unit timefrequencybi.jl. Its visualising and discussing these correlations that is often where the magic happens, as it exposes differences in understandings and perspectives on what all the pieces mean and how relate to each other. Or whether Evidence 4 is relevant given its relatively poor correlations with both Tactics and Aspiration. Or whether Tactic 2 is the best investment given its relatively poor correlations with both Strategies and Evidence. For example we might question why Strategy 3 only has Tactic 2 with an indirect/weak correlation. Remember, this is just a hypothesis, and by looking at the patterns of correlations around the X-Matrix we can see and discuss the overall coherence. Similarly, Strategy 1 has a direct/strong correlation with Aspiration 2, but Strategy 2 has no correlation, and Strategy 3 only has indirect/weak correlation. As do all the Strategies, which indicates high coherence. In other words this suggests that Strategy 1 contributes directly or strongly to Aspiration 1.

Here we can see that there is a direct or strong correlation between “Increase Revenue +10%” (Aspiration 1) and “Global Domination” (Strategy 1). (If using likelihood, a third variant would be needed, such as a circle with a dot in the middle). Filled circles represent direct or strong correlations, while empty circles represent indirect or weak correlations. I have tried letters and colours, and have recently settled on filled and empty circles, as in the example below. Having decided on an approach, a symbol is used in each cell to visualise the nature of each correlation. An X-Matrix where everything correlates with everything is usually too convenient and can be a sign of post-hoc justification. Whatever the language, there is always the option of none. This adds a third option, and therefore another level of complexity, but the language can be useful.

Likelihood – Is the correlation probable, possible or plausible.

This tends to be harder to describe because strong and weak are more subjective.

Strength – Is there a strong correlation between the elements, or is the correlation weak.

This tends to be easier to be objective about.

Directness – Can a direct correlation be explained, or is the correlation indirect via another factor (i.e.

There are a few approaches I have used to describe different types of correlation. What we are looking for is a messy coherence across all the pieces. Similarly applying Strategies and exhibiting Evidence both collectively contribute towards meeting Aspirations. Thus implementing Tactics collectively contribute towards applying Strategies and exhibiting Evidence. There may be a contribution of one to the other, but it is unlikely to be exclusive or immediate. Its important to emphasise that we are representing correlation, and not causation. This works through the shaded matrices in the corners of the X-Matrix – the ones that together form an X and give this A3 its name! Each cell in the matrices represents a correlation between two of the numbered elements. This creates a messy coherence – everything fits together, but without too much neatness or precision. Rather than consisting of simple hierarchical parent-child relationships, it allows more elaborate many-to-many relationships of varying types. One of the things I like about the X-Matrix is that it allows clarity of alignment, without relying on an overly analytical structure.

#Coherence x how to

I promised to say more about correlations in my last post on how to TASTE Success with the X-Matrix .