Regression Models Lab
Introduction to Machine Learning in Pharmaceutical Sciences (BP301T) · Semester 3 · Unit 2: Regression Models in Healthcare
Move a fictional dose-response line, inspect coefficients, residuals, MSE, RMSE, and R², and retain the non-predictive teaching boundary.
What this unit covers
Linear and multiple linear regression describe continuous teaching outcomes. Inspect coefficients, residuals, MSE, RMSE, and R² before reading a fictional dose-response, dissolution-rate, or PK-style relationship; a fitted model does not establish clinical validity.
Apply it
Fit an educational dose-response line, compare it with the best fit, inspect residuals and error metrics, and state why the result is not a real prediction.
How this lab works
A browser-rendered least-squares teaching fit updates residuals and error metrics from fictional points.
Data used
Fictional or browser-generated teaching inputs; no patient, proprietary manufacturing, or clinical-record data.
Limits
Educational simulation only. Outputs are not clinical advice, patient-specific predictions, release specifications, regulatory decisions, or professional competency evidence.