What term describes a situation where the correlation among independent variables in a regression model is excessive?

The term that describes a situation where there is an excessive correlation among independent variables in a regression model is known as multicollinearity. Multicollinearity occurs when two or more independent variables in the regression model are highly correlated, meaning they contain similar information about the variance in the dependent variable. This can lead to issues such as inflated standard errors for the coefficients, making it difficult to determine the influence of each independent variable on the dependent variable.

The presence of multicollinearity can undermine the statistical significance of the predictors, as it becomes challenging to assess their individual contributions. Identifying and addressing multicollinearity is crucial because it can distort the results of regression analyses, thus potentially leading to misleading conclusions.

In contrast, heteroscedasticity refers to the situation where the variability of the errors in a regression model is not constant across all levels of the independent variable. Auto-correlation pertains to the correlation of a variable with itself across different time intervals, which is especially a concern in time series data. Normality speaks to the distribution of the residuals in a regression model and does not relate to the correlation among independent variables.