Interpreting Regression Coefficients in Multiple Regression
In multiple regression models, for the interpreting regression coefficients, case, the unstandardized multiple regression coefficient is interpreted as the predicted change in $Y$ (i.e., the dependent variable abbreviated as DV) given a one-unit change in $X$ (i.e., the independent variable abbreviated as IV) while controlling for the other independent variables included in the equation.
- The regression coefficient in multiple regression is called the partial regression coefficient because the effects of the other independent variables have been statistically removed or taken out (“partially out”) of the relationship.
- If the standardized partial regression coefficient is being used, the coefficients can be compared for an indicator of the relative importance of the independent variables (i.e., the coefficient with the largest absolute value is the most important variable, the second is the second most important, and so on.)
Interpreting regression coefficients involves understanding the relationship between the IV(s) and the DV in a regression model.
- Magnitude: The coefficient tells about the change in the DV associated with a one-unit change in the IV, holding all other variables constant. For example, if the regression coefficient for IV (regressor) is 0.5, then it means that for every one-unit increase in that predictor, the DV is expected to increase by 0.5 units while keeping all else equal.
- Direction: The sign of the regression coefficient (+ or -) indicates the direction of the relationship between the IV and DV. A positive coefficient means that as the IV increases, the DV is expected to increase as well. A negative coefficient means that as the IV increases, the DV is expected to decrease.
- Statistical Significance: The statistical significance of the coefficient is important to consider. The significance of a regression coefficient tells about whether the relationship between the IV and the DV is likely to be due to chance or if it’s statistically meaningful. Generally, if the p-value of a regression coefficient is less than a chosen significance level (say 0.05), then that coefficient will be considered to be statistically significant.
- Interaction Effects: The relationship between an IV and the DV may depend on the value of another variable. In such cases, the interpretation of regression coefficients may involve the interaction effects, where the effect of one variable on the DV varies depending on the value of another variable.
- Context: Always interpret coefficients in the context of the specific problem being investigated. It is quite possible that a coefficient might not make practical sense without considering the nature of the data and the underlying phenomenon being studied.
Therefore, the interpretation of regression coefficients should be done carefully. The assumptions of the regression model, and the limitations of the data, should be considered. On the other hand, interpretation may differ based on the type of regression model being used (e.g., linear regression, logistic regression) and the specific research question being addressed.
How to interpret Coefficients of Simple Linear Regression Model
