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Multiple Linear Regression Explained Coefficients Assumptions Multicollinearity and Model Diagnostics

Multiple linear regression estimates how an outcome changes with several predictors while holding the other included variables constant. This practical guide explains coefficients, dummy variables, interactions, R-squared, residuals, multicollinearity, variance inflation factors, influential observations, prediction, common mistakes and a credit-risk example without inventing data.

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Moral Principles in Healthcare: Autonomy, Beneficence, Nonmaleficence, Justice, and Confidentiality

Healthcare ethics is not a contest in which one moral principle always defeats the others. Clinicians, researchers, and health organizations routinely face situations where several legitimate duties point in different directions. Respect for patient choice may conflict with concern about harm. Confidentiality may conflict with public-health reporting obligations. A treatment that offers benefit may also […]

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