A strong correlation (>0.85) among independent variables in a regression model violates which assumption?

A strong correlation among independent variables in a regression model indicates the presence of multicollinearity. Multicollinearity occurs when two or more independent variables are highly correlated with each other, which can cause problems in estimating the coefficients of the regression model. The estimated coefficients may become unstable, making it difficult to determine the individual effect of each predictor variable on the dependent variable. Moreover, multicollinearity can inflate the standard errors of the coefficients, leading to less reliable hypothesis tests and confidence intervals.

Understanding the implications of multicollinearity is crucial for interpreting regression results accurately. When multicollinearity is present, it can obscure the true relationships between independent and dependent variables, leading to misleading conclusions. Therefore, checking for multicollinearity is an essential part of regression diagnostics.

The other assumptions listed in the question pertain to different aspects of regression analysis. Homoscedasticity refers to the uniformity of variance of residuals, residual analysis examines the behavior of residuals to verify model assumptions, and the normal distribution of the dependent variable is related to the assumption of normality for the regression's error terms rather than the independent variables. Each of these assumptions plays a vital role in ensuring the validity of a regression model, but the specific concern