Regression Diagnostics
Regression Diagnostics groups the core ideas a learner needs at the 9 · evaluation, metrics & diagnostics stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Residual vs fitted plotResidual vs fitted plot is a communication and diagnostic technique that maps data or results into a visual form. A useful visual makes comparisons easy, exposes uncertainty and supports the analytical question rather than merely decorating the report.
02Residual distributionResidual distribution is a practical concept within Regression Diagnostics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03Heteroscedasticity intuitionHeteroscedasticity intuition is a practical concept within Regression Diagnostics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04Systematic biasSystematic bias is a practical concept within Regression Diagnostics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Influential observationsInfluential observations is a practical concept within Regression Diagnostics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
06Prediction interval coveragePrediction interval coverage is a practical concept within Regression Diagnostics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
07Segment-wise error analysisSegment-wise error analysis is a practical concept within Regression Diagnostics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.