What does multicollinearity in linear regression refer to?
Practice Questions
Q1
What does multicollinearity in linear regression refer to?
High correlation between the dependent variable and independent variables
High correlation among independent variables
Low variance in the dependent variable
Independence of errors
Questions & Step-by-Step Solutions
What does multicollinearity in linear regression refer to?
Step 1: Understand that in linear regression, we use independent variables to predict a dependent variable.
Step 2: Recognize that independent variables are the factors we think influence the outcome.
Step 3: Know that multicollinearity happens when two or more of these independent variables are very similar or related to each other.
Step 4: Realize that when independent variables are highly correlated, it can make it hard to determine their individual effects on the dependent variable.
Step 5: Understand that this can lead to unstable estimates of the coefficients, meaning the results can change a lot with small changes in the data.
