2017 Biostatistics

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Academic unit or major
Undergraduate major in Life Science and Technology
Yamada Takuji  Kotera Masaaki 
Course component(s)
Day/Period(Room No.)
Tue9-10(南4号館 情報ネットワーク演習室 第2演習室)  Fri9-10(南4号館 情報ネットワーク演習室 第2演習室)  
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Course description and aims

[Description] This course focuses on the basics of statistics utilized in life sciences. Topics include analysis of one-dimensional data, multi-dimensional data, testing statistical hypothesis, as well as statistical distributions using various probability density functions. By the end of the course, students experience the data analysis through the group work. This course also includes practices using the R program language.

[Aim] Understanding the basic concept of statistics, as well as acquiring the skills to analyze the practical data. This course includes lectures and exercises using the R programing language.

Student learning outcomes

By the end of this course, students will be able to:
・Understand the basic concepts of statistical analysis.
・Analyze practical data statistically using the R programing language


Statistics, the R programing language

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

At the beginning of each class, students are given (1) lecture for basic points, and (2) practical exercise with R. Toward the end of class, students should analyze practical data in group work

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction of bio statistics and programing language R Understand the definition and consept of statistics and R language.
Class 2 One dimensional data (1) Understand the basic fundamental statistics such as average, variance, standard deviation, and using R language as an exercise.
Class 3 One dimensional data (1) Understand various distributions and probability density functions.
Class 4 One dimensional data (1) Understand population, sample group, law of large numbers, central limit theorem.
Class 5 Hypothesis testing (1) Understand significance and testing of statistical hypothesis, as well as practice parametric tests such as t-test.
Class 6 Hypothesis testing (1) Practice parametric tests such as chi-squared test.
Class 7 Two dimensional data (1) Understand covariance and correlation.
Class 8 Two dimensional data (1) Understand regression analyses such as least-squares method.
Class 9 Two dimensional data (1) Understand correlation coefficient and regression line.
Class 10 Multi-dimensional data (1) Understand principal component analysis.
Class 11 Multi-dimensional data (1) Understand hierarchical cluster analysis.
Class 12 Multi-dimensional data (1) Understand multiple regression analysis.
Class 13 Practice of data analysis (1) Group work to analyze practical data using acquired knowledge in this course.
Class 14 Practice of data analysis (1) Group work to analyze practical data using acquired knowledge in this course.
Class 15 Group presentation Present the result of the group work.


Toukeigaku Nyuumon (Tokyo Daigaku Shuppankai), ISBN-13: 978-4130420655 (Japanese)

Reference books, course materials, etc.

Bioscience no Toukeigaku (Nankoudou), ISBN-13: 978-4524220366 (Japanese)

Assessment criteria and methods

Exercise 80%, group presentation 20%.

Related courses

  • LST.A246 : Bioinformatics

Prerequisites (i.e., required knowledge, skills, courses, etc.)


Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

Takuji Yamada (takuji[at]bio.titech.ac.jp),
Masaaki Kotera (maskot[at]bio.titech.ac.jp)

Office hours

Requires advance reservation

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