Which assumption must be satisfied in a multiple linear regression model to ensure accurate predictions?
Understand the Problem
The question is asking about the necessary assumptions that need to be met in a multiple linear regression model in order to make accurate predictions. It presents several options that relate to the characteristics of the data and model assumptions.
Answer
Linearity assumption.
The primary assumption that must be satisfied in a multiple linear regression model is the linearity assumption - the relationship between the dependent and independent variables should be linear.
Answer for screen readers
The primary assumption that must be satisfied in a multiple linear regression model is the linearity assumption - the relationship between the dependent and independent variables should be linear.
More Information
In addition to the linearity assumption, other important assumptions for accurate multiple linear regression predictions include the independence of errors, homoscedasticity (constant variance of errors), and normally distributed errors.
Tips
A common mistake is assuming linearity without visual inspection through scatterplots. It's crucial to check this assumption with real data to ensure the validity of the regression model.
Sources
- Understanding the Assumptions of Linear Regression Analysis - statisticssolutions.com
- Regression Model Assumptions | Introduction to Statistics - jmp.com
- Multiple Linear Regression | A Quick Guide (Examples) - Scribbr - scribbr.com
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