multiple linear regression

What are the Assumptions of Multiple Linear Regression?

MLR relies on several key assumptions:
Linearity: The relationship between the dependent and independent variables is linear.
Independence: Observations are independent of each other.
Homoscedasticity: The variance of the errors is constant across all levels of the independent variables.
Normality: The residuals (errors) are normally distributed.

Frequently asked queries:

What is Multiple Linear Regression?
Why Use Multiple Linear Regression in Epidemiology?
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What are the Assumptions of Multiple Linear Regression?
How to Interpret the Coefficients in Multiple Linear Regression?
What are the Steps Involved in Conducting Multiple Linear Regression?
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What are the Limitations of Multiple Linear Regression?
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