Differentiate multicollinearity, autocorrelation, heteroscedasticity, dummy variables
Multicollinearity refers to a situation in which two or more independent variables in a regression model are highly correlated with each other. This can cause issues in the model, such as difficulty in identifying the individual effects of each variable and unstable coefficient estimates.
Autocorrelation, also known as serial correlation, occurs when there is a correlation between the error terms of a regression model at different time points. This violates the assumption of independence of observations, which can lead to inefficient coefficient estimates and unreliable hypothesis testing.
Heteroscedasticity refers to the presence of unequal variances in the error terms of a regression model across different levels of the independent variable(s). This violates the assumption of constant variance, and can result in unreliable standard errors, biased coefficient estimates, and incorrect hypothesis testing.
Dummy variables, also called indicator variables or binary variables, are used to represent categorical variables in a regression model. They are typically used to code qualitative variables that cannot be directly included in a regression model. Dummy variables help capture the effect of different categories of a variable by creating separate variables (with a value of 0 or 1) for each category.