answersLogoWhite

0

The assumption of the criteria of least squares is that the residuals, or the differences between observed and predicted values, are normally distributed, have constant variance (homoscedasticity), and are independent of each other. This means that the errors in predictions should not show any patterns over time or across values of the independent variable, ensuring that the model is unbiased and that parameter estimates are efficient. Additionally, it assumes that the relationship between the dependent and independent variables is linear. These assumptions are crucial for the validity of statistical inferences made from the least squares estimates.

User Avatar

AnswerBot

2w ago

What else can I help you with?

Related Questions