What is heteroscedasticity , multicollinearity, autocorrelation
Heteroscedasticity refers to the unequal variability of errors (or residuals) in a regression model. In other words, it means that the spread or dispersion of the residuals is not constant across all levels of the independent variable(s). Heteroscedasticity violates one of the assumptions of the linear regression model, which assumes that the errors have constant variance (homoscedasticity).
Multicollinearity is a situation where there is a high correlation between two or more independent variables in a regression model. It means that some of the independent variables are highly correlated with each other, making it difficult to determine the individual effect of each variable on the dependent variable. Multicollinearity can cause issues in interpreting the coefficients of the variables and can lead to instability in the regression model.
Autocorrelation, also known as serial correlation, refers to the correlation between the error terms (or residuals) of a time series data set. It implies that the error terms are not independent of each other and may exhibit a pattern or structure. Autocorrelation violates the assumption of independence of observations in a regression model, which assumes that the error terms are not correlated. Autocorrelation can lead to inefficient coefficient estimates and incorrect hypothesis testing in regression analysis.