What method is used to examine the effects of multiple independent variables on a dependent variable in regression?

The chosen method, p-value analysis, is integral to regression analysis as it assesses the significance of each independent variable in explaining the variance in the dependent variable. In regression, the p-value helps to determine whether the relationship between the independent and dependent variables is statistically significant, indicating the likelihood that the observed relationship is not due to random chance.

When multiple independent variables are included in a regression model, p-values are calculated for each variable to ascertain their respective impacts. A low p-value (commonly below a threshold of 0.05) suggests strong evidence against the null hypothesis, meaning that the variable likely has a meaningful relationship with the dependent variable.

The other methods mentioned have specific applications but do not serve the primary purpose of evaluating the effects of multiple independent variables on a dependent variable in the context provided. Disaggregation is used for breaking down data into more detailed components, shift-share analysis assesses regional economic changes, and location quotient analysis measures the concentration of a particular variable in a region compared to a larger benchmark, none of which align directly with the function of examining multiple independent variables in a regression framework.