筑波大学システム情報工学研究科コンピュータサイエンス専攻科目一覧
Experiment Design in Computer Science
担当教員
Claus Aranha, Tetsuya Sakurai
電子メール caranha@cs.tsukuba.ac.jp
URL
オフィスアワー Office Hours by appointment through MANABA
科目番号 01CH740
分野 共通科目
基礎/専門の別 基礎科目
授業形態 Lectures
開講学期 Spring AB
時限 Friday 5,6
教室 3B301
キーワード Experiment Design, Statistical Testing, Statistics, Philosophy of Science, Ethics, R Language
Keyword Experiment Design, Statistical Testing, Statistics, Philosophy of Science, Ethics, R Language
前提条件

No particular requirements for registration.

This course expect students to be familiar with basic statistical concepts (Random Variables, Distributions, etc). The experiments in the course will use the R language, so familiarity with this language is helpful.

This course expect students to be familiar with basic concepts of statistics (Mean, Median, Standard deviation, Population, Random Variables, etc). We also encourage the students to study the R programming language to prepare for this course.

学位プログラム・コンピテンスとの関係 知の活用力,マネジメント能力,チームワーク力,国際性,研究力,倫理観
学習目標 At the end of the course, students should be able to:
  • Understand the reasoning behind experimental design in scientific research.
  • Prepare rigorous experimental design and planning for their own research.
  • Perform Statistical Significance and Power analysis on experimental data.
  • Evaluate other research based on its experimental quality.
概要

The collection and analysis of data through experiments is one of the cornerstones of the scientific method. In this course, we study the general philosophy and methods behind experimentalism: Why do we perform experiments, what is a good/rigorous experiment, how to plan and design a rigorous experiment, and how to perform statistical analysis on experimental data.

This course is centered around lectures with plenty of examples and study cases. The students will be invited to apply the techniques studied in this lecture to experiment of their own design.

授業計画
  • Class 1: Introduction - What is science, and what is a scientific experiment? What is rigorous research?
  • Class 2: Statistics review - Point Indicators and Interval Indicators (Random Variables, Means, Variances, Distributions, Confidence Intervals)
  • Class 3: Introduction to Inference Testing -- Hypothesis, Type Errors, Z testing.
  • Class 4: Introduction to Inference Testing -- Comparison Testing, Paired Testing
  • Class 5: Introduction to Inference Testing -- Equality Testing, Non-Parametric Testing
  • Class 6: Lecture Review and Case Study
  • Class 7: Power Analysis, Sample Size and Sample Choosing
  • Class 8: Multiple Comparison: Anova and post-hoc testing
  • Class 9: Blocking and Parameter Choice
  • Class 10: Lecture Review and Case Study
教科書 No designated textbook: The course will be based on lecture notes to be distributed electronically on Manaba
参考書
  • D.C. Montgomery, "Design and Analysis of Experiments", Wiley, 2005,
  • Felipe Campelo "Lecture Notes on Design and Analysis of Experiments", Online, 2018,
  • Other recommended readings on MANABA
成績評価

The grade is based on three individual reports.

Each report is a case study, where the student will have to: Design an experiment to answer a scientific question, obtain experimental data (from the instructor, or by performing an experiment), analyze the experimental data using the tools studied in the lecture, and prepare a report discussing the experimental conclusions.

The report is evaluated on the quality of the experiment design, the correctness of the statistical analysis, and the quality of the discussion of the results (note that positive or negative results do not factor in the grade of the report).

  • The first report is due before lecture 3, and worth 30% of the grade.
  • The second report is due before lecture 7, and worth 30% of the grade.
  • The third report is due by the date of the final exam and worth 40% of the grade.
TF・TA 1 TA is assigned for office hours.
その他の情報

Lecture in English

I expect the students to contribute with questions and discussions during the class. Additionally, I will recommend that the students use data from their own research to prepare the reports. Students who cannot provide their own data will be provided with sample data, however you will learn much more if you can use your own data.

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