When conducting a linear regression, is it true that you must have multicollinearity?

In the context of linear regression, multicollinearity refers to the situation where two or more predictor variables are highly correlated, leading to difficulties in estimating the relationships between each predictor and the outcome variable. However, multicollinearity is not a requirement for conducting linear regression; rather, it is something analysts strive to avoid, as it can distort the results of the regression analysis.

In fact, when multicollinearity is present, it can lead to inflated standard errors for the coefficients, making them less reliable and potentially leading to incorrect conclusions about the relationship between predictors and the outcome. Therefore, the assertion that multicollinearity must exist in a linear regression analysis is incorrect. A well-conducted regression analysis should aim for low multicollinearity among predictor variables to ensure the robustness and interpretability of the model results.