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 corre...
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