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流行病學與生物統計計算

112-1 開課加開
  • 備註
    本課程以英語授課。歡迎大學部高年級生選修，非同步遠距教學。拓析統計進階課程。大數據學程「程式資訊應用」領域選修。與李文宗合授
  • 修課限制

    • 限學士班三年級以上

  • 本校選課狀況

    載入中
  • 課程概述
    /*** This is a non-synchronous online course taught in English. ***/ /*** No auditors will be allowed. ***/ 在多數的生物統計課程中，老師時常介紹理論模式，繼而以統計軟體 (如SAS與R) 來分析資料。然而，在這兩個部分的中間卻存在著一個黑盒子。為能連接理論與軟體分析報表，本課程將介紹在統計模式中所牽涉的數值計算過程。同學將學習到矩陣運算、數值分析、蒙地卡羅模擬等。本課程涵蓋如何從統計分布來建構概似函數、求得羅吉斯迴歸與卜瓦松迴歸裡的最大概似估計值、求得精確信賴區間 (exact confidence interval)、以及如何為一個研究主題設計蒙地卡羅模擬等。 In most biostatistics courses, instructors usually introduce theoretical models and then analyze data with statistical software such as SAS and R. However, there is a black box between these two parts. To link statistical theory to software output, we will introduce the numerical computation process involved in statistical models. Students will learn matrix operations, numerical analyses, Monte Carlo simulations, etc. We will teach how to construct a log-likelihood function according to a statistical distribution, obtain maximum likelihood estimates from a logistic regression and a Poisson regression, find exact confidence intervals, design Monte Carlo simulations for a given research topic, etc.
  • 課程目標
    /*** This is a non-synchronous online course taught in English. ***/ /*** No auditors will be allowed. ***/ This course aims to inspire students’ interests in numerical computation regarding epidemiology and biostatistics and cultivate students’ critical thinking and logic in programming. This course is expected to facilitate students’ research in biostatistics, epidemiology, or related quantitative fields. It will also build students’ further understanding of quantitative epidemiology and biostatistics.
  • 課程要求
    /*** This is a non-synchronous online course taught in English. ***/ /*** No auditors will be allowed. ***/ 同學需先修過至少一門的 (生物) 統計學或流行病學課程。本課程以英語授課。 Prerequisite course: at least one course in biostatistics (or statistics) or epidemiology. The course will be offered in English.
  • 預期每週課後學習時數
    3 hours
  • Office Hour

    每週一下午 2-3 點 2-3 pm each Monday.

    *此 Office Hour 需要提前預約
  • 指定閱讀
    課程講義 Teachers' handouts
  • 參考書目
    1. Owen Jones, Robert Maillardet, and Andrew Robinson (2009). “Introduction to scientific programming and simulation using R”. Chapman & Hall/CRC. 2. Marcello Pagano and Kimberlee Gauvreau (2000). “Principles of Biostatistics”. 2nd edition. Duxbury Press.
  • 評量方式
    100%

    Weekly assignment and homework

    每週指定作業

  • 針對學生困難提供學生調整方式
    調整方式說明
    上課形式

    以錄影輔助

  • 課程進度
    9/7第 1 週R 的安裝與起始 (林菀俞) Install and start R
    9/14第 2 週函數程式編寫 (林菀俞) Programming with functions
    9/21第 3 週物件型態，程式邏輯，與 R 的迴圈運算 (林菀俞) R objects, programming logics, and R looping
    9/28第 4 週R的輸入輸出與繪圖 (林菀俞) Input, output, and plotting with R
    10/5第 5 週矩陣代數之介紹 (李文宗) Introduction to matrix algebra
    10/12第 6 週矩陣運算與線性代數 (李文宗) Matrix operations and linear algebra
    10/19第 7 週統計學中的矩陣代數 (李文宗) Matrix algebra in statistics
    10/26第 8 週靈巧的apply 家族函數與遞迴程式、矩陣運算指令 (林菀俞) The clever “apply” family, recursive programming, commands for matrix operations
    11/2第 9 週求函數解與確切信賴區間 (林菀俞) Find the root for a function; exact confidence intervals
    11/9第 10 週建構概似函數 (林菀俞) Construct a log-likelihood function
    11/16第 11 週為羅吉斯迴歸求解最大概似估計值 (林菀俞) Find the maximum likelihood estimates for a logistic regression
    11/23第 12 週為卜瓦松迴歸求解最大概似估計值 (林菀俞) Find the maximum likelihood estimates for a Poisson regression
    11/30第 13 週現代統計計算：蒙地卡羅模擬 (區間估計) (林菀俞) Modern statistical computing: Monte-Carlo simulations (Interval estimates)
    12/7第 14 週現代統計計算：蒙地卡羅模擬 (點估計) (林菀俞) Modern statistical computing: Monte-Carlo simulations (Point estimates)
    12/14第 15 週現代統計計算：蒙地卡羅模擬 (型一誤差率與檢力) (林菀俞) Modern statistical computing: Monte-Carlo simulations (Type I error rates and power)
    12/21第 16 週期末考週 (no class) Final exam week (no class)