シラバス情報
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教員名 ： Bishnu Kumar Adhikary
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授業科目名
Special Topics II Special Topics II
(英語名)
Special Topics II Special Topics II
科目区分
専門教育科目
General Course
対象学生
社会科学研究科
学年
学年指定なし
ﾅﾝﾊﾞﾘﾝｸﾞｺｰﾄﾞ
KCWMS5MCA1
単位数
2.00単位
ナンバリングコードは授業科目を管理する部局、学科、教養専門の別を表します。詳細は右上の？から別途マニュアルをダウンロードしてご確認ください。
授業の形態
講義 (Lecture)
開講時期
2024年度後期
(Fall semester)
担当教員
Bishnu Kumar Adhikary
所属
Graduate School of Social Sciences
授業での使用言語
英語
関連するＳＤＧｓ目標
目標1／目標2
ｵﾌｨｽｱﾜｰ・場所
10:00-17:00
Room A317 (Research Building 1) School of Economics and Management 連絡先
By appointment (adhikarykobejp@gmail.com)
対応するディプロマ・ポリシー(DP)・教職課程の学修目標
二重丸は最も関連するDP番号を、丸は関連するDPを示します。
学部DP
1◎／2◎／3◎
研究科DP
1◎／3◎
全学DP
1-1◎／2-1◎
教職課程の学修目標
目標１:磨き続ける力／目標３:協働する力／ー
講義目的・到達目標
This course introduces statistical tools for business and economic data analysis. First, the course focuses on univariate analysis, such as mean, mode, median, variance, standard deviation, correlation, and simple regression. Then, it discusses multivariate analysis, such as multiple regressions under different models. A particular focus is given to the time series and panel data analysis. This is an applied course and helpful for writing a thesis. After completing the course, students will learn to apply statistical tools and techniques for analyzing economic and financial data. In particular, this course will enhance students’ knowledge and skills in understanding, processing, and analyzing economic and financial data independently using different regression models.
授業のサブタイトル・キーワード
Statistic, parameter, mean, median, variance, sampling, regression, multiple regression, time series, panel data
講義内容・授業計画
Week 1
Introduction and overview of the course: Understanding financial and economic data- time series, cross-section, and panel data Week 2 Univariate analysis for economic data: Mean, mode, median, range, maxima, and minima Week 3 Univariate analysis for economic data: Variance, standard deviation, and correlation Week 4 Sampling Week 5 Hypothesis testing Week 6 Simple regression Week 7 Quizzes and assignment Week 8 Multiple regression under time series Week 9 The drawback of OLS, stationarity, and causality Week 10 ARDL, VEC, and VECM model Week 11 Multiple regression in Panel data Week 12 Pooled OLS Week 13 Fixed effect model and Random effect model Week 14 Review of assignments Week 15 Final Report 教科書
Business Statistics, 4e, 2018, Norean Sharpe, Richard De Veaux, and Paul Velleman, Pearson Education Ltd., ISBN: 978-0134705217
参考文献
事前・事後学習（予習・復習）の内容・時間の目安
Students must attend every class on time and actively participate in classroom discussions. In doing so, students need to study the PowerPoint slides in advance. The course includes assignments, quizzes, and a final report. Some assignments should be done in a team and to be presented in the classroom. Before starting every session, the instructor will supply all necessary reading materials and PowerPoints. So, students need to study 4 hours weekly to do well in the exam. Students should spend at least 60 hours outside the classroom to achieve good grades in this course.
アクティブ・ラーニングの内容
Course pedagogy includes lectures, assignments, quizzes, and a final report. Some assignments include group work and need to be presented in the classroom. Students should read assigned materials before attending every class. The instructor will supply the reading materials and PowerPoints in advance in the classroom. Students should attend every session on time.
成績評価の基準・方法
1. Class attendance 20%
2. Quizzes 20% 3. Assignment and presentation 20% 4. Final report 40% 課題・試験結果の開示方法
In the classroom
履修上の注意・履修要件
Students should have a basic knowledge of mathematics. Do not send or read text messages during class. In the case of an emergency, go outside to make calls, taking permission from the instructor.
実践的教育
備考
This is a foundation course for business research methods
英語版と日本語版との間に内容の相違が生じた場合は、日本語版を優先するものとします。
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