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The correlation between two observations of any neuropsychological variable was assumed to follow an autoregressive correlation model of order 1.
For stochastic kinetic models, the jump proposal corresponds to moves that change the number and the time of reactions that occur between two observations of the system.
Furthermore, in the random intercept model the correlation between two observations of a subject is constant and is equal to the intra-class correlation given by ρ = σ b 0 2 σ b 0 2 + σ ϵ 2 [ 19, 20].
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The kappa statistic is a statistical measure that calculates the agreement between two observations in excess of the amount of agreement that would arise purely by chance.
Table 2 reports the average rank changes in prices between two observations with a time lag of 1, 10, and 30 days in Barcelona, Madrid, Málaga, and Vigo.
This is of course the most widely used measure of distance between two observations.
The euclidean distance metric was selected as the measure of dissimilarity between two observations.
The first step in this approach is to define a measure of similarity between two observations.
Pearson's r is a value between -1.0 and 1.0 that describes the linear relationship between two observations [ 21] such that a correlation of 1.0 represents a perfect positive linear relationship and a correlation of -1.0 represents a perfect negative linear relationship [ 24].
A non-parametric correlation exists whenever the relation of order defined between two observations is similar across the two considered metrics.
Odds ratios and Fischer's exact (chi squared) tests were applied to estimate the strength of any association between two observations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com