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?
How is Multiple Linear Regression Different from Simple Linear Regression?
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?
What are the Common Applications of Multiple Linear Regression in Epidemiology?
What are the Limitations of Multiple Linear Regression?
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What are the Limitations of Epidemiological Evidence?
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