大学院
過去（2023年度）の授業の情報です
学内のオンライン授業の情報漏洩防止のため，URLやアカウント、教室の記載は削除しております。
最終更新日：2025年4月21日
授業計画や教室は変更となる可能性があるため、必ずUTASで最新の情報を確認して下さい。
UTASにアクセスできない方は、担当教員または部局教務へお問い合わせ下さい。
最終更新日：2025年4月21日
授業計画や教室は変更となる可能性があるため、必ずUTASで最新の情報を確認して下さい。
UTASにアクセスできない方は、担当教員または部局教務へお問い合わせ下さい。
Theory and Practice for Advanced Statistics
The main objective of this course is to review the linear model and introduce some of its extensions, namely mixed linear models, generalized linear models, nonlinear regression. The last three lectures will introduce Bayesian principles, from a parametric and nonparametric point of view. Focus will be on explaining the theory in a synthetic manner and apprehend how the proposed models can be estimated using R. A side goal is to get familiar with the statistical vocabulary and be able to understand what black box/packages are actually doing to help acquiring a critical eye on the results of biological or agricultural data analysis.
時間割／共通科目コード
コース名
教員
学期
時限
3912171
Theory and Practice for Advanced Statistics
岩田 洋佳
月曜5限
マイリストに追加
マイリストから削除
講義使用言語
英語
単位
1
実務経験のある教員による授業科目
NO
他学部履修
可
開講所属
農学生命科学研究科
授業計画
●Lecture 1: From simple linear regression to Ancova analysis.
●Lecture 2: Linear mixed models
●Lecture 3: Generalized Linear Models, ridge/lasso regression, and introduction to Generalized Additive Models
●Lecture 4: Nonlinear regression and
●Lecture 5: Bayesian main principles
●Lecture 6: introduction to Bayesian nonparametrics
●Lecture 7: MCMC approaches.
授業の方法
There will be no specific textbook, and lectures will be given on the basis of resumes distributed each time. Three small reports presenting applications of the models presented in class to personal datasets will be required to consolidate the basic knowledge learned in the class.
成績評価方法
Comprehensive evaluation will be made based on reports (100%). However, no grade will be given for low attendance.
教科書
None in particular
参考書
None in particular
履修上の注意
By taking this course, students will gain knowledge of statistical vocabulary and advanced approaches to analyze various biological datasets. They will deepen their understanding of the underlying statistics, ideally using their own datasets. The amount and type of data in agriculture and biology will continue to increase in the future, and knowledge and understanding of these methods will become increasingly important in future agricultural and biological research.
その他
Lectures are conducted as a face-to-face lecture (Bldg.1, Lecture Room No.4)
Language used: English (Medium of instrucion: E、Course materials: E)
Lecturer writing this syllabus: Tressou Jessica
