Best (but oft-forgotten) practices: checking assumptions concerning regression residuals.

Area of Science:

• Statistics
• Biostatistics
• Econometrics

Background:

• Least squares regression is a widely used statistical method.
• Valid confidence intervals (CIs) and P values depend on specific assumptions about the model's residuals.
• Violated assumptions can lead to biased estimates and reduced statistical power.

Purpose of the Study:

• To outline methods for assessing the key assumptions of least squares regression residuals.
• To provide guidance on appropriate actions when these assumptions are not met.

Main Methods:

• The abstract discusses the definition of residuals in least squares regression.
• It highlights the assumptions of independence, normality, and constant variance for residuals.
• The text implies a review of diagnostic techniques and remedial strategies.

Main Results:

• Assumption violations in least squares regression can compromise the validity of statistical results.
• Methods exist to evaluate these assumptions.
• Strategies are available to address violations when they occur.

Conclusions:

• Proper assessment and handling of residual assumptions are crucial for reliable least squares regression.
• Understanding and addressing assumption violations enhance the integrity of statistical findings.