日本語 English
開講年度/ Academic YearAcademic Year |
20232023 |
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 |
多彩な学び/主題別/多彩な学び/主題別多彩な学び/主題別 |
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) |
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授業形式/ Class StyleCampus |
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校地/ CampusCampus |
池袋/IkebukuroIkebukuro |
学期/ SemesterSemester |
秋学期他/Fall OthersFall Others |
曜日時限・教室/ DayPeriod・RoomDayPeriod・Room |
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単位/ CreditCredit |
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 |
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配当年次/ Grade (Year) RequiredGrade (Year) Required |
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 |
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他学部履修可否/ Acceptance of Other CollegesAcceptance of Other Colleges |
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履修中止可否/ course cancellationcourse cancellation |
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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 |
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学位授与方針との関連/ Relationship with Degree PolicyRelationship with Degree Policy |
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備考/ NotesNotes |
・オンデマンド授業 ・TOEIC®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 |
テキスト用コード/ Text CodeText Code |
FB149 |
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.
※Please refer to Japanese Page for details including evaluations, textbooks and others.
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) |
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平常点 (In-class Points) | 100 |
4 homeworks ×25%(100%) |
備考 (Notes) | ||
その他 (Others) | |||||
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The content presented online corresponds to the text |
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.