By M. Taillard
This publication info very easily and for even the main amateur of strength analysts not just how one can practice analytics which describe what's taking place, are expecting what will take place, and optimize responses, but in addition locations those analytics within the context of proactive procedure improvement.
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Additional info for Analytics and Modern Warfare: Dominance by the Numbers
In other words, by testing the combat capabilities of a battalion before and after some new form of training, it is possible to determine whether any changes were the result of the new training, or whether it was the result of chance. This helps to resolve questions about the possibility of whether soldiers were simply having a good/ bad day at some point during the assessment, for example. The dependent samples t-test is calculated as follows: t ¤D n¤ D 2 D 2 n 1 In the case of dependent samples t-test, the D refers to the difference in score pairings.
Just start with the percentage change of the independent variable, then measure the percentage change in the dependent variable, which happens as a result. For example, you could start with a percent increase in the total volume of people and resources being mobilized, and measure the amount of time required to complete the mobilization process in response; you could measure the percentage response in opposition morale to change in the percentage difference of troops, which could be modeled as: Δdt/Δmo, wherein dt is the amount of differential of troops in an imminent combat situation, and mo is the amount of morale maintained by the opposition.
So, in seeking to calculate x, a very simple model might look like this: x = Cov[a, b]/[a(y) + b(z)], wherein a and b are statistical units—either known values or the value generated by some other sort of calculation—while y and z are multipliers that determine the amount of weight given to either a or b in the calculation. Cov[a, b] refers to the degree to which a and b vary in their values together, rather than idiosyncratically, which is called covariance. Just as a and b might be any known value or measurement, statistical tests can also be inserted there, depending on what you are trying to accomplish.