Why is it important to check the assumptions of a multiple linear regression model?

To ensure the validity of the model
To increase the complexity of the model
To increase the number of observations
To reduce the R-squared value

Checking the assumptions of a multiple linear regression model (like linearity, independence, normality, and homoscedasticity) is crucial to ensure the validity of the model and its estimates. Violations of these assumptions can lead to biased or inefficient estimates, and inferences made from such models could be misleading.

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