日本語 English
| 開講年度/ Academic YearAcademic Year |
20242024 |
| 科目設置学部/ CollegeCollege |
経済学部/College of EconomicsCollege of Economics |
| 科目コード等/ Course CodeCourse Code |
BX438/BX438BX438 |
| テーマ・サブタイトル等/ Theme・SubtitleTheme・Subtitle |
Introduction to statistics |
| 授業形態/ Class FormatClass Format |
対面（全回対面）/Face to face (all classes are face-to-face)Face to face (all classes are face-to-face) |
| 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 |
池袋/IkebukuroIkebukuro |
| 学期/ SemesterSemester |
秋学期/Fall semesterFall semester |
| 曜日時限・教室/ DayPeriod・RoomDayPeriod・Room |
火1･5504/Tue.1･5504 Tue.1･5504 |
| 単位/ CreditCredit |
22 |
| 科目ナンバリング/ Course NumberCourse Number |
ECX2311 |
| 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/ 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 |
|
| 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 |
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 |
|
| テキスト用コード/ Text CodeText Code |
BX438 |
This course provides an opportunity for students to learn elementary statistics, by playing with the data and to make statistical inferences by visualising the data.
The course will start with the introduction to R and its tools. We will explore the data to better understand the data before we summarise and describe the data. We will then try to understand the relationships among the data with correlation and regression analysis, followed by confirming our understanding of these relationships with hypotheses testing. We conclude with a gentle detour into machine learning by predicting Titanic passenger survivals.
We will rely on R and its packages tools to automate the generation of the results of the statistical analysis, embedding it with charts, tables and written inferences and conclusions into a report in pdf.
After the class, students should have the confidence to independently conduct their own data analysis to draw statistical inferences. Furthermore, students will also have obtained skills in using R and its packages and RStudio. These skills will be helpful to students, whether they continue to pursue a research or business career, as many business-related jobs now require data analysis and statistical inference skills.
※Please refer to Japanese Page for details including evaluations, textbooks and others.
This course provides an opportunity for students to learn elementary statistics, by playing with the data and to make statistical inferences by visualising the data.
The course will start with the introduction to R and its tools. We will explore the data to better understand the data before we summarise and describe the data. We will then try to understand the relationships among the data with correlation and regression analysis, followed by confirming our understanding of these relationships with hypotheses testing. We conclude with a gentle detour into machine learning by predicting Titanic passenger survivals.
We will rely on R and its packages tools to automate the generation of the results of the statistical analysis, embedding it with charts, tables and written inferences and conclusions into a report in pdf.
After the class, students should have the confidence to independently conduct their own data analysis to draw statistical inferences. Furthermore, students will also have obtained skills in using R and its packages and RStudio. These skills will be helpful to students, whether they continue to pursue a research or business career, as many business-related jobs now require data analysis and statistical inference skills.
| 1 | Introduction |
| 2 | Installing R (and its packages) and Rstudio |
| 3 | Exploratory data analysis (1) |
| 4 | Exploratory data analysis (2) |
| 5 | Descriptive statistics (1) |
| 6 | Descriptive statistics (2) |
| 7 | Correlation |
| 8 | Regression analysis (1) |
| 9 | Regression analysis (2) |
| 10 | Hypothesis testing (1) |
| 11 | Hypothesis testing (2) |
| 12 | Machine learning: Titanic survival (1) |
| 13 | Machine learning: Titanic survival (2) |
| 14 | Summary & conclusions |
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
This course will be conducted using the flipped classroom method. A certain amount of study will be required before class to prepare students to for the workshop activity during the class.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
Mini tests(20%) Class presentations(10%) Assignment 1(20%) Assignment 2(20%) 最終レポート(Final Report)(30%) |
| 備考 (Notes) | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| The study material for this course are freely available from the internet. This includes textbooks available under the Creative Commons license. |
It would be helpful if students bring along an internet accessible device (tablet, laptop) for use in the workshop.
This course will be conducted using the flipped classroom method. A certain amount of study will be required before class to prepare students to for the workshop activity during the class. The class will begin with a short lecture to give an overview of the content of the lesson, followed by a mini test to evaluate if the student has completed the required pre-study before coming to class and understood the content of the short lecture. The remainder of the class will consist of a workshop or lab, where students will engage in hands-on learning of statistical concepts using R, supervised by the Lecturer.