## Step-by-Step Solution ### Step 1: Understanding Multicollinearity - **Multicollinearity** occurs when two or more predictor variables in a regression model are highly correlated. - This makes it difficult to determine the individual effect of each predictor on the dependent variable. ### Step 2: Analyzing Each Option #### Option A: Adding more predictor variables - **Effect:** Adding more predictors can actually increase the risk of multicollinearity, not reduce it. #### Option B: Increasing the sample size - **Effect:** Increasing sample size may stabilize coefficient estimates, but it does **not** directly address multicollinearity among predictors. #### Option C: Removing highly correlated predictors - **Effect:** If you remove predictors that are highly correlated with others, you reduce multicollinearity because the remaining variables are less correlated. #### Option D: Using a higher significance level - **Effect:** Changing the significance level does not affect multicollinearity; it only changes the threshold for statistical significance. ### Step 3: Selecting the Correct Answer - The only method among the options that directly addresses the issue of multicollinearity is **removing highly correlated predictors**. --- ## Final Answer **C. Removing highly correlated predictors** This is the correct method for reducing multicollinearity in regression analysis.