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Including the count of female CD as a control variable removes confounding by forces that affect the incidence of CD equally across sex (e.g., maternal age, ethnicity, changes in clinical culture, delivery on demand).
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The models that included collinear variables were refitted with each variable removed to determine misleading variations in coefficients.
The changes in OR values reflect the influence of the variable removed from the model on the particular association.
All variables with p < 0.1 were initially put into the logistic regression model and then least significant variable removed until all variables left in model had p < 0.1.
Individual variables were removed in a backward stepwise manner until the change in −2log likelihood in reduced models was less than expected for the associated degrees of freedom of the variable removed.
If the regression model was tested with the driver self-regulation status variable removed, there was still no significant association between the visual variables and depressive symptoms score.
ΔR: Change in correlation coefficients contributed by the variable (removing effect of age); β: unstandardized regression coefficient with CI (Confidence interval); Stand.
We then fit a series of multivariate models each with one secondary explanatory variable removed, always noting the value of the β coefficient associated with incarceration.
When a coefficient remains in front of the variable, remove it by dividing both sides by that coefficient.
When additional numbers are left on the side of the equation containing the variable, remove these numerical values by adding or subtracting them to both sides.
case to ND random variables, removes the identically distributed condition, and expands the ranges and, respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com