By M. Taillard
This ebook information very easily and for even the main amateur of strength analysts not just easy methods to practice analytics which describe what's taking place, are expecting what will take place, and optimize responses, but additionally locations those analytics within the context of proactive procedure improvement.
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Extra info for Analytics and Modern Warfare: Dominance by the Numbers
An effect size calculation that is more versatile, and as a result more commonly used, is Cohen’s d. What makes it more versatile is that it is bidirectional; rather than measuring against a control group, it measures the difference between two groups using a pooled standard deviation, as follows: M2 n1 1 s 2 1 M1 n1 1 s22 n1 n2 2 Note that the numerator is still the difference between the means of the two groups, but that the denominator now incorporates the root of the standard deviation of both groups as a ratio of the degrees of freedom.
The other two types of averages are not subject to this same type of distortion, the first of which is known as a median. The median is the exact center of a data set; simply line-up the values from smallest to largest, then find the middle value. If there is an even number of values, so that there is no single observed value in the middle, then the median is found by taking the two values in the center and calculating the mean between them. The median has the benefit of being, by definition, in the exact center of the data set, that does not necessarily mean that it will be in the exact center of the values; it is guaranteed that 50 percent of the values will exist above the median, and 50 percent below the median, but the values themselves may cluster at some point in the data set, or there may be extreme values.
As noted near the beginning of this chapter, models are composed of several pieces: the statistical units, the covariance between those statistical units, and the degree of response to which the calculation responds 30 M Analytics and Modern Warfare to each statistical unit. 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.