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We accordingly used binary stepwise logistic regression analysis, with a dichotomous dependent variable, selecting for each test the model that maximizes the chi-square likelihood ratio.
We performed multiple regression analyses, also including quadratic terms for each independent variable, selecting models from ranked Akaike Information Criterion AICC) values [ 53].
Let X1 be the random variable selecting a base k chosen among 2, 3, …, n randomly and independently with the uniform distribution in order to form a base pair (1, k).
Let X t +1 be the random variable selecting a base k chosen among X t + 2, X t + 3, …, n randomly and independently with the uniform distribution in order to form a base pair (X t + 1, k).
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LOS was the outcome variable selected to estimate cost.
The independent variable selected for the optimization was COD removal.
For 'temperate' soils PAWC was the only variable selected by the tree building algorithm.
In both models, the only variable selected was the interaction term between military experience and business experience where the R-squared of the regression model was 0.1 (meaning the model only explained about 10% of the variability in presidential performance).
This variable is a competitive variable selected after removing W-Mg.
The only other variable selected was 'dechallenge', which was included with small coefficients in two iterations.
Moreover, when a variable is selected, the list of attributes associated with this variable, typically provided as a selector, reflects the type of the variable selected.
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CEO of Professional Science Editing for Scientists @ prosciediting.com