![實驗設計與統計分析[隨]](/Content/images/placeholder/course-credit-card.png)
實驗設計與統計分析[隨]
NT$ 15,000
(如欲報名此一課程，新生須先註冊學員)
優惠說明
- 學分班報名費500元，除下述優待資格免收報名費外，每一學年（第1學期、第2學期）須繳交1次。
(1)本校教職員生及校友者，(2)同一學期報名多班學分班者，(3)下學期繼續報名者。 - 若系統未出現「報名費折扣」，請勿繳費，請來信ee@ntua.edu.tw或上班時間（週一至週五08:30-17:00）來電告知及核對身份確定後，請自行刪除原訂單再重新選課。
報名須知
- 課程若額滿，請來信ee@ntua.edu.tw排候補；「候補學員」在115年2月2日(一)10:00前，都未接到遞補通知，表示無名額，請改選其他課程。
- 學員於修習期間應遵守本校及開課單位的相關規定，如有報名資格不符、患有或疑似患有法定傳染病、不當行為影響教學及學員上課，經通知仍未改善者，得取銷其修習資格。
- 報名時間：
- 『舊生優先』Mail人工報名：自網路公布日起，至115年6月8日(一)10:00止。
※曾於、「114-2」及「115-1暑期班」報名學分班課程，或本校教職員生及校友者，優先報名，一律使用Mail（ee@ntua.edu.tw）通訊報名，並於規定期限內前完成繳費，逾期者刪除選課。 - 『所有學員』開放網路報名：自115年6月8日(一)14:00起，至115年8月31日(一)10:00止；最遲於115年9月21日(一)前完成報名及繳費，逾期不予受理。
- 『舊生優先』Mail人工報名：自網路公布日起，至115年6月8日(一)10:00止。
- 報名資格：大學畢業以上（二、五專須畢業滿3年，三專須畢業滿2年），並檢附畢業證書。
- 招生簡章：115-1學、碩士課程學分班招生簡章《點我連結》；115-1「學期學分班」－開學注意事項《尚未公佈》。
※課程名稱有[上]或[下]為學年課程，[隨]為附讀於正規學制，[A]、[B]、…為課程班別，非指不同課程。 - 收費標準及繳交資料：應繳金額= 報名費500元（學分班1學年繳交1次，本校教職員生及校友免繳）＋課程總費用。
※繳費帳號請登入本系統，在「常用服務>>檢視選課、繳費帳號及繳費情形」查詢；繳費1個小時後，亦可在此查詢繳費情形。
※除未滿18歲報名學士課程，需繳交高中（職）以上「畢業證書」影本外，不用再繳交身分證、繳費收據、報名表…等資料。 - 退費標準：
- 報名繳費後至開課日前：退還繳交費用9成，不含報名費。
- 開課日後至課程三分之一前：退還繳交費用5成，不含報名費。
- 逾課程三分之一：不退費。
- 報名繳費後至開課日前：退還繳交費用9成，不含報名費。
- 其他相關事宜，請參閱本校推教中心「常見問題」《點我連結》，或來電(02)2272-3568或ee@ntua.edu.tw洽詢。
課程資訊
授課時間
(五)19:25-22:10
學年/學期
學期課
開課單位
圖文系
招生名額
5 位
課程起訖
2026/09/11 ~ 2027/01/08
學分/時數
3.0 / 54 hrs
上課教室
教研803
師資簡介
袁宇熙 老師
學歷：
國立臺灣師範大學工業教育學系博士
經歷：
國立臺灣藝術大學圖文系專任教授
課程簡介
課程概要
本課程是為加強研究生的研究方法所設計。統計方法(或研究方法)大致上可分為：如何取得資料與如何分析資料。一般對統計方法的訓練主要在於後者：如何分析資料，但是對於如何取得資料缺乏有系統的認識。因此，本課程將從最基本的定義與概念介紹抽樣方法與實驗設計，希望學生對統計學基礎所發展出之如何取得資料的理論有基本的認識。
教學目標
本課程教學目標為：
1. 了解資料取得方法與管道
2. 熟悉資料分析技術與注意事項
3. 介紹抽樣方法與合宜的樣本數
4. 說明實驗設計與實驗效度的威脅
5. 學生能夠完成一套實驗設計與實證資料分析的研究
課程要求(含課堂外作業要求)
依據課程進度完成指定作業及分析報告
評量方式
出缺席15%
課堂報告及指定作業15%
期中評量30%
期末報告40%
教科書
-
書名:自編講義
作者: / 出版社: / 出版日:
參考書籍或網址
-
書名:實驗設計與分析(Montgomery : Design and Analysis of Experiments 10/E)
作者:巫佳煌、唐麗英、黎正中 / 出版社:高立圖書 / 出版日:2023/02/01 -
書名:Experimental Design and Analysis
作者:Howard J. Seltman / 出版社:STAT (https://www.stat.cmu.edu/~hseltman/309/Book/Book.pdf) / 出版日:July 11, 201 -
書名:Understanding Statistics and Experimental Design
作者:Michael H. Herzog , Gregory Francis , Aaron Clarke / 出版社:Springer (https://link.springer.com/book/10.1007/978-3-030-03499-3) / 出版日:2019.04.18 -
書名:Fundamentals of Statistical Experimental Design and Analysis
作者:Robert G. Easterling / 出版社:Wiley (https://www.wiley.com/en-us/Fundamentals+of+Statistical+Experimental+Design+and+Ana / 出版日:2015.09.08 -
書名:Statistical Design and Analysis of Experiments: With Applications to Engineering and Science
作者:Robert L. Mason, Richard F. Gunst, James L. Hess / 出版社:Wiley (https://onlinelibrary.wiley.com/doi/book/10.1002/0471458503) / 出版日:31 January 2
課程內容與進度
周次與日期
內容
預定進度
習作考試
備註
1 - 2026/09/07-2026/09/13
提醒學生使用正版教科書，尊重智慧財產權[Introduction and motivation]
Review (population and samples, observational versus experimental studies)
Introduction to experimental designs
Terminology and key concepts of experimental design
Requirements for a good experiment
Review (population and samples, observational versus experimental studies)
Introduction to experimental designs
Terminology and key concepts of experimental design
Requirements for a good experiment
提醒學生使用正版教科書，尊重智慧財產權
2 - 2026/09/14-2026/09/20
[Review of key statistics concepts]
Sampling variability
Hypothesis testing
Confidence intervals
Sampling variability
Hypothesis testing
Confidence intervals
3 - 2026/09/21-2026/09/27
[Review of key statistics concepts]
Examples of basic tests
two sample t-test and Welch’s t-test
chi-square test for contingency table
permutation test
Examples of basic tests
two sample t-test and Welch’s t-test
chi-square test for contingency table
permutation test
4 - 2026/09/28-2026/10/04
[Completely randomized designs with one factor]
Introduction to one-way analysis of variance
F-statistic and sum of square decomposition
Parametrization
Model assumptions
Introduction to one-way analysis of variance
F-statistic and sum of square decomposition
Parametrization
Model assumptions
5 - 2026/10/05-2026/10/11
[Contrasts and multiple testing]
Contrasts
Multiple testing, family-wise error rate and false discovery rate
Contrasts
Multiple testing, family-wise error rate and false discovery rate
6 - 2026/10/12-2026/10/18
[Contrasts and multiple testing]
Methods for control: methods for ANOVA (Tukey, Dunnett and Scheffé)
General methods (Bonferroni, Holm and Benjamini-Hochberg)
Methods for control: methods for ANOVA (Tukey, Dunnett and Scheffé)
General methods (Bonferroni, Holm and Benjamini-Hochberg)
7 - 2026/10/19-2026/10/25
[Completely randomized designs with two between-subject factors]
Interactions
Marginal and conditional contrasts
Interactions
Marginal and conditional contrasts
8 - 2026/10/26-2026/11/01
[Repeated measures and within-subject two factor designs]
Unbalanced designs and implications for inference
Repeated measures and within-subject correlation
Unbalanced designs and implications for inference
Repeated measures and within-subject correlation
9 - 2026/11/02-2026/11/08
[Repeated measures and within-subject two factor designs]
Tests of sphericity
Tests of sphericity
10 - 2026/11/09-2026/11/15
[Multiway analysis of variance]
Models with within-subject and between-subject factors
Three-way ANOVA: simple, marginal and conditional means
Models with within-subject and between-subject factors
Three-way ANOVA: simple, marginal and conditional means
11 - 2026/11/16-2026/11/22
[Analysis of covariance]
Concept: Analysis of covariance
Pre-post experiments
Concept: Analysis of covariance
Pre-post experiments
12 - 2026/11/23-2026/11/29
[Analysis of covariance]
Linear regression models
Assumption of equal slopes
Linear regression models
Assumption of equal slopes
13 - 2026/11/30-2026/12/06
[Effect sizes and power]
Effect sizes
Interplay between sample size, effect and power
Power calculations
Effect sizes
Interplay between sample size, effect and power
Power calculations
14 - 2026/12/07-2026/12/13
[Mixed models]
Fixed vs random effects
Blocking factors
Linear mixed models
Fixed vs random effects
Blocking factors
Linear mixed models
15 - 2026/12/14-2026/12/20
[Mixed models]
Model specification: crossed and nested factors
Showcase: replication and meta-analysis
Model specification: crossed and nested factors
Showcase: replication and meta-analysis
16 - 2026/12/21-2026/12/27
[Replication crisis and introduction to causal inference]
Replication crisis and reproducibility
Statistical fallacies
Solutions to replication crisis
Directed acyclic graphs
Types of association: causation, mediation, confounding and collision
Assumptions for causal inference
Replication crisis and reproducibility
Statistical fallacies
Solutions to replication crisis
Directed acyclic graphs
Types of association: causation, mediation, confounding and collision
Assumptions for causal inference
17 - 2026/12/28-2027/01/03
[Linear mediation and moderation]
Baron–Kenny linear mediation model: Sobol’s statistic
Nonparametric bootstrap
Linear mediation model
Mediation and interactions
Baron–Kenny linear mediation model: Sobol’s statistic
Nonparametric bootstrap
Linear mediation model
Mediation and interactions
18 - 2027/01/04-2027/01/10
[Categorical data analysis and final review]
Contingency tables and count data: goodness-of-fit, independence, symmetry
Nonparametric tests
Q&A, final review and practice exam
Contingency tables and count data: goodness-of-fit, independence, symmetry
Nonparametric tests
Q&A, final review and practice exam