授業の目的 【日本語】
Goals of the Course(JPN) | | |
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授業の目的 【英語】
Goals of the Course | | | Students will learn various regression methods using cross-sectional data. Topics include multiple regression model, nonlinear regression model, and regression with a binary dependent variable. |
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到達目標 【日本語】
Objectives of the Course(JPN) | | At the end of the course, all students will be able to:
1. Demonstrate an understanding of basic regression models.
2. Determine and run appropriate regression model for a given data set and research question and interpret the regression results. |
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授業の内容や構成
Course Content / Plan | | 1. (04/11) Introduction & Simple linear regression: Part 1 (ch.4)
2. (04/18) Simple linear regression: Part 2 (ch.4)
3. (04/22, Saturday) Simple linear regression: Part 3 (ch.4)
4. (04/25) Hypothesis tests and CI in simple linar regression: Part 1 (ch.5)
5. (05/09) Hypothesis tests and CI in simple linar regression: Part 2 (ch.5)
6. (05/16) Multiple regression: Part 1 (ch.6)
7. (05/23) Multiple regression: Part 2 (ch.6)
8. (05/30) Hypothesis tests and CI in multiple regression: Part 1 (ch.7)
9. (06/13) Hypothesis tests and CI in multiple regression: Part 2 (ch.7)
10. (06/20) Term project instruction
11. (06/27) Nonlinear regression: Part 1 (ch.8)
12. (07/04) Nonlinear regression: Part 1 (ch.8)
13. (07/11) Regression with a binary dependent variable: Part 1 (ch.11)
14. (07/18) Regression with a binary dependent variable: Part 2 (ch.11)
15. (07/25) Term project discussion
16. (08/01) Closing session |
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履修条件・関連する科目
Course Prerequisites and Related Courses | | | Students are expected to know basic statistics and basic operations of RStudio. |
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成績評価の方法と基準
Course Evaluation Method and Criteria | | 1. Term paper (70%): Detailed instructions will be distributed around the 10th week. Late submission will be accepted with a 10% penalty per day (24-hour period).
2. In-class activities (30%): There will be multiple in-class activities throughout the semester. There will be no announcement on the dates of in-class acitivities, and there will be no makeups for missed in-class activities. Therefore, it is the studen's responsibility to attend class regularly.
The following is the grading schema. Students must score at least 60 (C-) to pass this course.
Score Grade
95～100 A+
80～94.99 A
70 – 79.99 B
65 – 69.99 C
60 – 64.99 C-
0 – 59.99 F |
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教科書・参考書
Textbook/Reference Book | | This course requires the following free online textbook:
Hanck et al. "Introduction to Econometrics with R." 2021. Link: https://www.econometrics-with-r.org/index.html |
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課外学習等（授業時間外学習の指示）
Study Load(Self-directed Learning Outside Course Hours) | | | Students are expected to review course materials taught in each class. |
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注意事項
Notice for Students | | | Students must bring their computers to every class to use RStudio. Smartphones and tablets cannot be used for this purpose. Instructions for the installment of R will be provided during the first class meeting. |
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授業開講形態等
Lecture format, etc. | | | In principle, face-to-face lessons only. |
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遠隔授業（オンデマンド型）で行う場合の追加措置
Additional measures for remote class (on-demand class) | | |
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質問への対応方法
Office hour | | |
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