Module 2 - Lesson 1: Trolley problems, and predictions using regression and least squares

[turukawa]

ETHICS

Evaluate methods for balancing right and wrong in trolley problems.

Introduction to trolley problems and the conflicting ethical considerations they raise. Foundation for the module.
Example: Would you kill the fat man? Torture a kidnapper to find a child? Should Facebook hide Nazis to “pretend” they don’t exist? YouTube, terrorism and research.

CURATION

Differentiate between data which should be archived, and which should be deleted.

Personal data, financial transactions, research / cohort data, legal responsibilities.

ANALYSIS

Predict trends and future data with regressions, least squares, and least squares regressions.

Correlation, regression and least squares;
Outliers and influence for linear regression and least squares regression.

PRESENTATION

Apply line and scatter techniques to demonstrate and visualise predictive models.

Data visualisation methods for presenting uncertainty and variance.

---

CASE STUDY

Orlistat to reduce fat absorption; consider drugs and cost vs efficiency, NHS NICE.