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When discussing the assumptions underlying the Ordinary Least Squares (OLS) method, we are essentially referring to the conditions that need to be satisfied for OLS estimators to be valid and efficient. These assumptions are crucial for ensuring that the results obtained from an OLS regression are reliable and interpretable. Below is a detailed explanation of each assumption:
Linearity of Relationship
No Multicollinearity
Normality of Residuals
Homoscedasticity
No Autocorrelation
No Endogeneity
Each of these assumptions plays a critical role in ensuring that the OLS regression provides valid, unbiased, and efficient estimates. Understanding these assumptions allows data scientists to diagnose potential issues in their models and apply necessary corrections, such as transforming variables, adding interaction terms, or using alternative estimation methods like Generalized Least Squares (GLS) or Instrumental Variables (IV) regression.