2025 (Current Year) Faculty Courses School of Life Science and Technology Undergraduate major in Life Science and Technology
Biostatistics
- Academic unit or major
- Undergraduate major in Life Science and Technology
- Instructor(s)
- Takuji Yamada / Kengo Sato / Koichiro Uriu
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Mon (W9-324(W933)) / 1-2 Thu (W9-324(W933))
- Class
- -
- Course Code
- LST.A241
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Apr 11, 2025
- Language
- Japanese
Syllabus
Course overview and goals
[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.
[Aim] Understanding the basic concept of statistics, as well as acquiring the skills to analyze the practical data.
Course description and aims
By the end of this course, students will be able to:
・Understand the basic concepts of statistical analysis.
Keywords
Statistics, Biostatistics
Competencies
- 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 lecture for basic points. Toward the end of class, students should analyze practical data in group work
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Relationship between Life Sciences and Statistics | Overview of the definition of statistics, its significance in life sciences, and its relevance to research. Learning Outcome: Explain the role and necessity of statistics in life sciences. |
| Class 2 | Types of Data and Descriptive Statistics | Learn about data scales (nominal, ordinal, interval, ratio) and descriptive statistics (mean, variance, etc.). Learning Outcome: Select and explain appropriate statistical measures based on data types. |
| Class 3 | Data Visualization and Understanding Distributions | Learn to interpret histograms, box plots, scatter plots, and distribution characteristics. Learning Outcome: Appropriately visualize data distributions and explain their features. |
| Class 4 | Fundamentals of Probability and Probability Distributions | Learn definitions of probability, addition and multiplication rules, and characteristics of representative probability distributions. Learning Outcome: Choose appropriate probability distributions for experiments or observations and explain their characteristics. |
| Class 5 | Basics of Sampling and Estimation | Study the concepts of populations and samples, standard error, and confidence intervals. Learning Outcome: Estimate populations from samples and understand and explain the meaning of confidence intervals. |
| Class 6 | Concepts of Hypothesis Testing | Learn about hypothesis testing, significance levels, p-values, and Type I and Type II errors. Learning Outcome: Understand hypothesis testing structure and correctly explain the meaning of statistical significance. |
| Class 7 | Principles and Applications of t-tests | Learn methods for testing differences between two group means (t-tests) and effect sizes. Learning Outcome: Distinguish between paired/unpaired t-tests and interpret test results appropriately. |
| Class 8 | Analysis of Variance (ANOVA) | Learn about one-way ANOVA for multiple-group comparisons and how to interpret results. Learning Outcome: Understand ANOVA structure and determine group differences from results. |
| Class 9 | Problems of Multiple Comparisons and Countermeasures | Study errors resulting from multiple comparisons and correction methods such as Tukey and Bonferroni corrections. Learning Outcome: Explain risks of multiple comparisons and select appropriate correction methods. |
| Class 10 | Correlation and Simple Regression Analysis | Learn to construct and interpret correlation coefficients and simple regression models. Learning Outcome: Quantify relationships between variables and explain the significance of simple regression models. |
| Class 11 | Multiple Regression Analysis and Variable Selection | Study structures of multiple regression analysis, partial regression coefficients, and multicollinearity. Learning Outcome: Interpret models with multiple variables and appropriately select variables. |
| Class 12 | Basics of Bayesian Statistics | Explore Bayesian estimation concepts (prior and posterior distributions) and differences from frequentist approaches. Learning Outcome: Understand the basics of Bayesian thinking and explain simple applications. |
| Class 13 | Statistical Model Comparison and Selection | Learn model selection and performance evaluation using AIC, BIC, cross-validation, and generalization performance. Learning Outcome: Compare multiple models' performances and select better models. |
| Class 14 | Practical Applications and Summary of Statistical Literacy | Comprehensive review of interpreting statistical results, reading scientific papers, and integration with AI. Learning Outcome: Understand how to apply statistics practically and effectively utilize statistical knowledge. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Toukeigaku Nyuumon (Tokyo Daigaku Shuppankai), ISBN-13: 978-4130420655 (Japanese)
Reference books, course materials, etc.
Bioscience no Toukeigaku (Nankoudou), ISBN-13: 978-4524220366 (Japanese)
Evaluation methods and criteria
Grades will be determined based on the results of short tests administered at the end of each class session. The final grade will be calculated as the average score of all tests.
Related courses
- LST.A246 ： Bioinformatics
Prerequisites
none
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Takuji Yamada (takuji[at]bio.titech.ac.jp),
Office hours
Requires advance reservation