Assumptions like linearity, no perfect multicollinearity, and homoscedasticity are vital in regression analysis from "summary" of Introduction to Econometrics by Christopher Dougherty
Assumptions like linearity, no perfect multicollinearity, and homoscedasticity play a crucial role in regression analysis. Linearity assumes that the relationship between the dependent variable and the independent variables is linear. This assumption is important because if the relationship is non-linear, the model will not accurately capture the true relationship between the variables. Perfect multicollinearity refers to a situation where two or more independent variables are perfectly correlated with each other. This can cause problems in regression analysis because it makes it impossible to estimate the individual effects of the independent variables on the dependent variable. Homoscedasticity is another important assumption in regression analysis. It states that the variance of the error term is constant across all values of the independent variables. If this assumption is violated, it can lead to biased and inefficient estimates of the coefficients in the regression model.- These assumptions are vital because they ensure that the regression model is correctly specified and that the estimated coefficients are unbiased and efficient. Violating these assumptions can lead to misleading results and undermine the validity of the regression analysis. Thus, it is essential to carefully check these assumptions before interpreting the results of a regression analysis.
