Chart 1. Most important parameters in regression analyses and their interpretations.

Parameter	Linear regression	Logistic regression
Direction and strength of the association between the independent variable and the dependent variable (outcome)	Beta coefficient: Describes the (expected) average change in the outcome variable for each one-unit change in the independent variable for continuous variables, or the average change in the outcome variable for one category of the independent variable compared with a reference category for categorical variables	OR: The OR for a continuous independent variable is interpreted as the change in the odds of the outcome occurring for every one-unit increase in the independent variable The OR for categorical independent variables is interpreted as the increase or decrease in odds between two categories (e.g., men vs women) OR = 1: no association; OR > 1: positive association or risk factor; and OR < 1: negative association or protective factor
Example (for a continuous independent variable)	The expected increase in FEV1 for each centimeter increase in height	The expected increase in the odds of death for each increase of one year of age among patients with sepsis
Example (for a categorical independent variable)	The expected increase in FEV1 for men compared with women with the same height and age	The expected increase in the odds of death for men compared with women among COVID-19 patients
Precision of the estimate	The 95% CI of the beta coefficient	The 95%CI of the OR
Statistical significance	The p value (significant when < 0.05)	The p value (significant when < 0.05)