日本語 English
| 開講年度/ Academic YearAcademic Year |
20242024 |
| 科目設置学部/ CollegeCollege |
ビジネスデザイン研究科/Graduate School of Business AdministrationGraduate School of Business Administration |
| 科目コード等/ Course CodeCourse Code |
VL157/VL157VL157 |
| テーマ・サブタイトル等/ Theme・SubtitleTheme・Subtitle |
|
| 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) |
Online instruction will begin during the first week of the semester. |
| 授業形式/ Class StyleCampus |
講義/LectureLecture |
| 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 |
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 |
BDS5801 |
| 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 |
履修登録システムの『他学部・他研究科履修不許可科目一覧』で確認してください。 |
| 履修中止可否/ Course CancellationCourse Cancellation |
－（履修中止制度なし/ No system 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 |
Learning objectives include:
Conceptualize, develop and interpret the results of intermediate-level statistical analyses.
Further develop students' understanding of the concept of statistical inference and its application to more advanced statistical methods.
Further develop students' understanding of the rationale and methods of preparing data for statistical analysis.
Further develop students' ability to identify appropriate statistical analysis for specific types of data and research questions.
Review of key concepts and methods introduced in Statistics Essentials 1.
Multiple linear regression: testing and evaluating models and variables.
Factorial ANOVA: analysis using two or more independent variables.
Other methods: logistic regression, factor analysis, path analysis.
| 1 | Introduction and course overview |
| 2 | Review of concepts and methods covered in Statistics Essentials 1 course. |
| 3 | Further understanding of correlation and variance. |
| 4 | Multiple linear regression: basic concepts and method. |
| 5 | Multiple linear regression: interpreting models and variables. |
| 6 | Multiple linear regression: model building methods. |
| 7 | ANOVA: from one-way to factorial designs. |
| 8 | ANOVA: main effects and interaction. |
| 9 | Logistic regression: underlying concept and relationship to multiple linear regression. |
| 10 | Logistic regression: interpreting results. |
| 11 | Path analysis: underlying concept and relationship to multiple linear regression. |
| 12 | Factor analysis: underlying concept and applications |
| 13 | Examples and applications |
| 14 | Review |
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
| 補足事項 (Supplementary Items) |
|---|
| The basic method will be on-demand video lectures consisting of narrated slides. |
Readings, practice problems, and homework as assigned.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
最終テスト(Final Test)(40%) Homework and quizzes(60%) |
| 備考 (Notes) | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Students are not required to purchase a textbook for this course. |
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Journal articles and other readings will be provided by the instructor. |
Students should be comfortable with online instruction, including watching lecture videos, downloading course materials, and uploading assignments.
Students should have access to a desktop, laptop or tablet that they can use to access the online class material and complete/upload assignments.
This course follows Statistics Essentials 1 and will further build students' understanding of and experience with key methods of statistical analysis, particularly multiple linear regression and factorial analysis of variance (ANOVA). It will also introduce students to more advanced methods of analysis such as factor analysis, logistic regression and path analysis.
Learning objectives include:
Conceptualize, develop and interpret the results of intermediate-level statistical analyses.
Further develop students' understanding of the concept of statistical inference and its application to more advanced statistical methods.
Further develop students' understanding of the rationale and methods of preparing data for statistical analysis.
Further develop students' ability to identify appropriate statistical analysis for specific types of data and research questions.
The general topics to be covered will include:
Review of key concepts and methods introduced in Statistics Essentials 1.
Multiple linear regression: testing and evaluating models and variables.
Factorial ANOVA: analysis using two or more independent variables.
Other methods: logistic regression, factor analysis, path analysis.
| 1 | Introduction and course overview |
| 2 | Review of concepts and methods covered in Statistics Essentials 1 course. |
| 3 | Further understanding of correlation and variance. |
| 4 | Multiple linear regression: basic concepts and method. |
| 5 | Multiple linear regression: interpreting models and variables. |
| 6 | Multiple linear regression: model building methods. |
| 7 | ANOVA: from one-way to factorial designs. |
| 8 | ANOVA: main effects and interaction. |
| 9 | Logistic regression: underlying concept and relationship to multiple linear regression. |
| 10 | Logistic regression: interpreting results. |
| 11 | Path analysis: underlying concept and relationship to multiple linear regression. |
| 12 | Factor analysis: underlying concept and applications |
| 13 | Examples and applications |
| 14 | Review |
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
| 補足事項 (Supplementary Items) |
|---|
| The basic method will be on-demand video lectures consisting of narrated slides. |
Readings, practice problems, and homework as assigned.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
最終テスト(Final Test)(40%) Homework and quizzes(60%) |
| 備考 (Notes) | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Students are not required to purchase a textbook for this course. |
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Journal articles and other readings will be provided by the instructor. |
Students should be comfortable with online instruction, including watching lecture videos, downloading course materials, and uploading assignments.
Students should have access to a desktop, laptop or tablet that they can use to access the online class material and complete/upload assignments.