What measure(s) of the strength and direction of association can we get from a linear regression model?

The regression coefficient, commonly referred to as beta, is a fundamental output of a linear regression model. This coefficient quantifies the strength and direction of the relationship between the independent variable(s) and the dependent variable. Specifically, the value of the regression coefficient indicates the expected change in the dependent variable for a one-unit increase in the independent variable, holding all other variables constant.

For instance, if a beta coefficient of 2 is obtained for a variable in the model, it implies that for each one-unit increase in that variable, the dependent variable is expected to increase by 2 units. Furthermore, the sign of the coefficient (positive or negative) indicates the direction of the relationship; a positive coefficient suggests a positive association, while a negative coefficient suggests an inverse association.

Other options presented do not serve as measures of strength and direction of association in the context of linear regression. Risk ratios are primarily used in epidemiological studies to compare the likelihood of outcomes between two groups. One-way ANOVA is a technique for comparing means across multiple groups but does not provide a direct measure of association like regression coefficients do. The F-statistic is a measure derived from the overall model fit in regression analysis, indicating whether the model explains a significant portion of the variability in the