Uncertainty exists in many real-life problems, ranging from stock returns to sport results to medication effects to election outcomes. Statistics offers methods to handle uncertainty with a higher precision. This course introduces ways to define, model and forecast uncertainty through real-life examples and counterintuitive phenomena. Topics include exchange paradox, Simpson’s paradox, linear regression model, non-parametric regression model, historical simulation, and k-mean clustering.

不確定性存在於許多現實生活中的問題，例子涵蓋股票回報、運動結果、藥物效果、選舉結果等。統計科學提供了具更高準定性的方法，以處理不確定性的問題。本課程以實際示例和違反直覺的現象引導，來定義、模型和預測不確定性。主題包括替換悖論，辛普森悖論，線性迴歸模型，非參數迴歸模型，歷史模擬法，k 平均演算法。