日本語 English
| 開講年度/ Academic YearAcademic Year |
20242024 |
| 科目設置学部/ CollegeCollege |
全学共通科目・全学共通カリキュラム（総合系）/University-wide Liberal Arts Courses (Comprehensive Courses)University-wide Liberal Arts Courses (Comprehensive Courses) |
| 科目コード等/ Course CodeCourse Code |
FB999/FB999FB999 |
| テーマ・サブタイトル等/ Theme・SubtitleTheme・Subtitle |
多変量解析入門 |
| 授業形態/ Class FormatClass Format |
オンデマンド（全回オンデマンド）/On-demand (all classes are on-demand)On-demand (all classes are on-demand) |
| 授業形態（補足事項）/ Class Format (Supplementary Items)Class Format (Supplementary Items) |
|
| 授業形式/ Class StyleCampus |
講義/LectureLecture |
| 校地/ CampusCampus |
他/OtherOther |
| 学期/ SemesterSemester |
秋学期他/Fall OthersFall Others |
| 曜日時限・教室/ DayPeriod・RoomDayPeriod・Room |
ログインして教室を表示する（Log in to view the classrooms.） |
| 単位/ CreditsCredits |
22 |
| 科目ナンバリング/ Course NumberCourse Number |
CMP2231 |
| 使用言語/ LanguageLanguage |
英語/EnglishEnglish |
| 履修登録方法/ Class Registration MethodClass Registration Method |
科目コード登録/Course Code RegistrationCourse Code Registration |
| 配当年次/ Assigned YearAssigned Year |
配当年次は開講学部のR Guideに掲載している科目表で確認してください。配当年次は開講学部のR Guideに掲載している科目表で確認してください。 |
| 先修規定/ Prerequisite RegulationsPrerequisite Regulations |
|
| 他学部履修可否/ Acceptance of Other CollegesAcceptance of Other Colleges |
|
| 履修中止可否/ Course CancellationCourse Cancellation |
〇（履修中止可/ Eligible for cancellation） |
| オンライン授業60単位制限対象科目/ Online Classes Subject to 60-Credit Upper LimitOnline Classes Subject to 60-Credit Upper Limit |
○○ |
| 学位授与方針との関連/ Relationship with Degree PolicyRelationship with Degree Policy |
各授業科目は、学部・研究科の定める学位授与方針（DP）や教育課程編成の方針（CP）に基づき、カリキュラム上に配置されています。詳細はカリキュラム・マップで確認することができます。 |
| 備考/ NotesNotes |
・大学院所属学生対象科目 ・学部所属学生履修不可 |
We will learn the basic ideas, representative methods, and usage methods in society.
We will learn the basic concepts and representative methods of multivariate analysis. In particular, we will describe (1) methods for prediction and factor search, and (2) methods for organizing and classifying complex information. Furthermore, through analysis exercises using the statistical analysis language R, students will understand the usage cases and roles of these methods.
| 1 | What is multivariate analysis? |
| 2 | Review of descriptive statistics and inferential statistics |
| 3 | Correlation coefficient and partial correlation coefficient |
| 4 | Multiple regression analysis (1): From simple regression analysis to multiple regression analysis |
| 5 | Multiple regression analysis (2): Concept of multiple regression analysis |
| 6 | Multiple regression analysis (3): Dummy variables and cautions for regression analysis |
| 7 | Binomial logistic regression analysis |
| 8 | Two-way analysis of variance |
| 9 | Analysis of the triple cross tabulation table |
| 10 | Factor analysis (1): Concept of factor analysis |
| 11 | Factor Analysis (2): Factor Rotation and Cautions on Use |
| 12 | Principal component analysis |
| 13 | Cluster analysis |
| 14 | Structural equation modeling |
板書 /Writing on the Board
スライド（パワーポイント等）の使用 /Slides (PowerPoint, etc.)
上記以外の視聴覚教材の使用 /Audiovisual Materials Other than Those Listed Above
個人発表 /Individual Presentations
グループ発表 /Group Presentations
ディスカッション・ディベート /Discussion/Debate
実技・実習・実験 /Practicum/Experiments/Practical Training
学内の教室外施設の利用 /Use of On-Campus Facilities Outside the Classroom
校外実習・フィールドワーク /Field Work
上記いずれも用いない予定 /None of the above
Please read the materials and related books and check for any unclear points. Please review the procedures for calculating the statistics you have learned, and solidify your understanding of their properties. The contents of the video are just an introduction to the vast ocean of multivariate analysis, and there is a wealth of statistical theory and application examples that are not fully explained. Do not be satisfied with the video, but organize your own questions and arguments about the content to deepen your understanding.The approximate learning time is 240 minutes per class, including watching the videos.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
4 homeworks ×25%(100%) |
| 備考 (Notes) | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| The content presented online corresponds to the text |
| No | 著者名 (Author/Editor) | 書籍名 (Title) | 出版社 (Publisher) | 出版年 (Date) | ISBN/ISSN |
|---|---|---|---|---|---|
| 1 | 山田剛史，杉澤武俊，村井潤一郎 | 『Rによるやさしい統計学』 | オーム社 | 2008 | |
| 2 | 村井潤一郎 | 『初めてのR――ごく初歩の操作から統計解析の導入まで』 | 北大路書房 | 2015 | |
| 3 | 中村永友 | 『Rで学ぶデータサイエンス2 多次元データ解析法』 | 共立出版 | 2010 | |
| 4 | Fox, John, and Sanford Weisberg | An R Companion to Applied Regression | SAGE | 2011 (2nd ed.) |
It is desirable that students have already taken lectures on introductory statistics, statistical testing, and statistical estimation offered as other subjects. This course is based on the assumption that students have already acquired a foundation in these subjects as basic knowledge.
As tasks involving various analyses will be assigned, it is preferable for students to be comfortable with computer operations. Even if there is a lack of confidence in this area, a positive attitude and willingness to engage are necessary.
Use the statistical analysis software R.
This course corresponds to subject E, "Methods of Multivariate Analysis," which is designated as a subject for social researchers by the Japan Association for Social Research. Due to the nature of this subject, knowledge of basic statistics, which is the content of subject C, D, is assumed. Students who are not sure of their knowledge should review the course by themselves. Students are required to log in to Canvas LMS in advance and check the specific instructions for the class.If you have any questions about the class content, please use the schooling sessions held twice during the semester.
・F科目中級（外国語による総合系科目）
・TOEIC®L&R 550点相当以上の英語力を有していることを前提に授業を実施する
データに潜む重要な情報を明らかにする方法として多変量解析を位置づけ，基本的な考え方，代表的な手法，および社会における活用法を理解する。
We will learn the basic ideas, representative methods, and usage methods in society.
多変量解析の基本的な考え方と代表的な手法を習得する。特に，(1)予測・要因探求のための手法および(2)複雑な情報をまとめ分類するための手法について解説を行う。さらに、統計解析言語Rを用いた分析演習を通じて、これらの手法の活用事例や役割を理解する。
We will learn the basic concepts and representative methods of multivariate analysis. In particular, we will describe (1) methods for prediction and factor search, and (2) methods for organizing and classifying complex information. Furthermore, through analysis exercises using the statistical analysis language R, students will understand the usage cases and roles of these methods.
| 1 | What is multivariate analysis? |
| 2 | Review of descriptive statistics and inferential statistics |
| 3 | Correlation coefficient and partial correlation coefficient |
| 4 | Multiple regression analysis (1): From simple regression analysis to multiple regression analysis |
| 5 | Multiple regression analysis (2): Concept of multiple regression analysis |
| 6 | Multiple regression analysis (3): Dummy variables and cautions for regression analysis |
| 7 | Binomial logistic regression analysis |
| 8 | Two-way analysis of variance |
| 9 | Analysis of the triple cross tabulation table |
| 10 | Factor analysis (1): Concept of factor analysis |
| 11 | Factor Analysis (2): Factor Rotation and Cautions on Use |
| 12 | Principal component analysis |
| 13 | Cluster analysis |
| 14 | Structural equation modeling |
板書 /Writing on the Board
スライド（パワーポイント等）の使用 /Slides (PowerPoint, etc.)
上記以外の視聴覚教材の使用 /Audiovisual Materials Other than Those Listed Above
個人発表 /Individual Presentations
グループ発表 /Group Presentations
ディスカッション・ディベート /Discussion/Debate
実技・実習・実験 /Practicum/Experiments/Practical Training
学内の教室外施設の利用 /Use of On-Campus Facilities Outside the Classroom
校外実習・フィールドワーク /Field Work
上記いずれも用いない予定 /None of the above
Please read the materials and related books and check for any unclear points. Please review the procedures for calculating the statistics you have learned, and solidify your understanding of their properties. The contents of the video are just an introduction to the vast ocean of multivariate analysis, and there is a wealth of statistical theory and application examples that are not fully explained. Do not be satisfied with the video, but organize your own questions and arguments about the content to deepen your understanding.The approximate learning time is 240 minutes per class, including watching the videos.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
4 homeworks ×25%(100%) |
| 備考 (Notes) | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| The content presented online corresponds to the text |
| No | 著者名 (Author/Editor) | 書籍名 (Title) | 出版社 (Publisher) | 出版年 (Date) | ISBN/ISSN |
|---|---|---|---|---|---|
| 1 | 山田剛史，杉澤武俊，村井潤一郎 | 『Rによるやさしい統計学』 | オーム社 | 2008 | |
| 2 | 村井潤一郎 | 『初めてのR――ごく初歩の操作から統計解析の導入まで』 | 北大路書房 | 2015 | |
| 3 | 中村永友 | 『Rで学ぶデータサイエンス2 多次元データ解析法』 | 共立出版 | 2010 | |
| 4 | Fox, John, and Sanford Weisberg | An R Companion to Applied Regression | SAGE | 2011 (2nd ed.) |
It is desirable that students have already taken lectures on introductory statistics, statistical testing, and statistical estimation offered as other subjects. This course is based on the assumption that students have already acquired a foundation in these subjects as basic knowledge.
As tasks involving various analyses will be assigned, it is preferable for students to be comfortable with computer operations. Even if there is a lack of confidence in this area, a positive attitude and willingness to engage are necessary.
Use the statistical analysis software R.
This course corresponds to subject E, "Methods of Multivariate Analysis," which is designated as a subject for social researchers by the Japan Association for Social Research. Due to the nature of this subject, knowledge of basic statistics, which is the content of subject C, D, is assumed. Students who are not sure of their knowledge should review the course by themselves. Students are required to log in to Canvas LMS in advance and check the specific instructions for the class.If you have any questions about the class content, please use the schooling sessions held twice during the semester.
・F科目中級（外国語による総合系科目）
・TOEIC®L&R 550点相当以上の英語力を有していることを前提に授業を実施する