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A linear relationship between variables forms the backbone of regression analysis, crucial for accurate predictions.
Error normality is key to unbiased results and confidence in regression outcomes.
Consistent error variance across data ensures the precision of model predictions.
Identifying and mitigating multicollinearity is essential for model stability and clarity.
Scaling and normalization can significantly enhance linear regression results.
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Scatterplots and residuals analysis confirm the essential linear relationship in regression.
Histograms and Q-Q plots are tools to ensure residuals follow a normal distribution.
The Breusch-Pagan test checks for consistent variance in residuals, a cornerstone of reliable models.
Independent observations are crucial to avoid biased estimates, enhancing prediction reliability.
Variance Inflation Factor (VIF) and correlation matrices are key to identifying multicollinearity issues.
Access the full article on Assumptions in Linear Regression for in-depth insights and applications.