| 上課日期起 Start Date |
上課日期迄 End Date |
上課時間起 Start Time |
上課時間迄 End Time |
時數 Duration Time(hr) |
教室／容量 Room/capacity |
授課教師 Teacher |
課程進度 progression |
備註 Note |
|---|---|---|---|---|---|---|---|---|
| 2026-09-10 | 2026-09-10 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Introduction to biostatistics | |
| 2026-09-17 | 2026-09-17 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Descriptive statistics | |
| 2026-09-24 | 2026-09-24 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Probability and conditional probability | |
| 2026-10-01 | 2026-10-01 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Probability distributions(I) | |
| 2026-10-08 | 2026-10-08 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Probability distributions(II) & Sampling distribution | |
| 2026-10-15 | 2026-10-15 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Point estimation and central limit theorem | |
| 2026-10-22 | 2026-10-22 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Interval estimation and t-distribution | |
| 2026-10-23 | 2026-10-23 | 13:20:00 | 14:10:00 | 1 | 施銘杰 | Midterm take-home exam | ||
| 2026-10-29 | 2026-10-29 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Hypothesis testing / Inference from one sample | |
| 2026-11-05 | 2026-11-05 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Inference from two samples | |
| 2026-11-12 | 2026-11-12 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Correlation and simple linear regression | |
| 2026-11-19 | 2026-11-19 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Multiple linear regression and ANOVA & Causal inference and regression | |
| 2026-11-26 | 2026-11-26 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Inferences from contingency tables | |
| 2026-12-10 | 2026-12-10 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Regression for binary and count outcomes | |
| 2026-12-17 | 2026-12-17 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Nonparametric analysis & Introduction to survival analysis | |
| 2026-12-17 | 2026-12-17 | 17:30:00 | 18:20:00 | 1 | CII育成318/40 | 施銘杰 | 科技在醫療的應用 | 科技在醫療的應用 |
| 2026-12-24 | 2026-12-24 | 15:30:00 | 17:20:00 | 2 | CII育成318/40 | 施銘杰 | Interactive oral assessment | |
| 2026-12-24 | 2026-12-24 | 17:30:00 | 18:20:00 | 1 | 施銘杰 | Final take-home exam |
本課程主要面向對統計方法分析生物醫學資料有興趣的大學部學生。課程前半部將著重統計概念的建立，了解為何需要利用統計方法分析資料，以及推論統計的邏輯基礎。後半部將介紹常用之統計檢定與回歸方法，並輔以因果推論的理論框架介紹，讓學生了解針對有興趣的研究議題，如何選擇適當的統計模型進行資料分析後，解讀分析結果並給出結論。課程並附有電腦實作課，以供學生實地演練生物醫學資料之統計分析。
生物統計（Biostatistics）；描述性統計（Descriptive statistics）；推論性統計（Inferential statistics）；假說檢定（Hypothesis testing）；區間估計（Interval estimation）；迴歸分析（Regression analysis）；因果推論（Causal inference）
This 2-credit course is designed for undergraduate students interested in analysis of biomedical data with statistical models.
In the first part of the course, we will look into the theoretical
foundation of statistics and demonstrate how descriptive and inferential statistics can facilitate data summarization and evidence generation.
In the second part, we will introduce hypothesis tests and regression models commonly used in the biomedical literature. We will elaborate how to choose between the tests and models based on the question and data at hand, to interpret the analysis results and draw conclusions. We will also introduce the conceptual framework of causal inference and demonstrate how regression models can help us estimate the causal effect of interest.
There will be several computer laboratory tutorials using SPSS to carry out the analyses described in the lectures.
There are no pre-requisites for this course.
| # | 類別 Type |
版本 Version |
書名 Title |
出版年 Year of publication |
作者 Author |
出版者 Publishers |
ISBN | 單價 Price |
圖書館館藏 Library collections |
備註 Note |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 參考書籍 Refer Books | 2 | Biostatistics for the biological and health sciences | 2017 | Triola, M. M., Triola, M. F., & Roy, J. | Pearson | 0 | 無 | 無 |
| # | 標準 | 佔比 | 備註 |
|---|---|---|---|
| 1 | Personal in-class performance | 10% | |
| 2 | Personal assignments | 25% | |
| 3 | Group assignment | 15% | |
| 4 | Midterm Exam | 25% | |
| 5 | Final exam | 25% |
Students are allowed to use generative AI to explore topics strengthen arguments, and polish their writing in presentations and assignments, as long as they provide disclosure on where and how generative AI are used.