Introductory Statistics for Economics

Introductory Statistics for Economics

専門教育科目

ー

国際商経学部

学年指定なし

KCCBG1MCA7

2.00単位

講義・演習 (Lecture/Seminar)

2025年度後期

(Fall semester)

Jean-Baptiste M.B. SANFO

School of Economics and Management

英語

目標1

Before or after class, in the classroom

1◎／2〇／3〇

ー

ー

ー

【Course Objectives】
Statistics is a powerful tool used to make sense of data, uncover patterns, and inform decision-making. In economics, statistical skills are essential for interpreting market trends, evaluating policies, and conducting empirical research. This course provides a foundational understanding of statistics and its application to economic analysis. Throughout this course, we will explore key statistical concepts, methods, and techniques that are integral to understanding and solving real-world economic problems. We will focus on practical applications by emphasizing how statistical methods are used in economic research, policy analysis, and business decision-making.

【Learning Outcome】
By the end of this course, you will have developed critical skills to:
1. Demonstrate understanding of basic concepts of descriptive and inferential statistics
2. Apply basic concepts of descriptive and inferential statistics
3. Analyze economic data using statistical methods
4. Use statistical software to perform data analysis and present findings clearly and effectively.
5. Interpret and critically evaluate statistical results in economic research

Mean, variance,  probability, sample, population, correlation, regression

I will first present each topic theoretically to ensure an understanding of the necessary foundational concepts and terminology. I will also demonstrate some concepts using R, and we will discuss the output.

Lecture 1: Guidance and Introduction to Statistics
Lecture 2: Displaying Descriptive Statistics
Lecture 3: Calculating Descriptive Statistics
Lecture 4:  Introduction to Probabilities
Lecture 5: Discrete Probability Distributions
Lecture 6: Continuous Probability Distributions
Lecture 7: Mid-term evaluation
Lecture 8: Sampling and Sampling Distributions
Lecture 9:  Confidence Intervals
Lecture 10:  Hypothesis Testing for a Single Population
Lecture 11:  Hypothesis Tests Comparing Two Populations
Lecture 12:  Analysis of Variance (ANOVA) Procedures
Lecture 13:  Chi-Square Tests
Lecture 14:  Correlation and Simple Linear Regression I
Lecture 15:  Correlation and Simple Linear Regression II

Final evaluation
The content of the course may vary depending on the pace of the progress.

Computer Use: Students will use their own computers during some lectures (TBA).

Policy on the Use of Generative AI:

Students are expected to follow the instructions of the instructor regarding the use of generative AI. While it is acceptable to use generative AI as a supplementary tool for activities such as writing reports and conducting case searches, it is crucial to verify the accuracy of the content produced. Students should ensure that facts are checked and that sources and references are properly acknowledged. The output from generative AI must not be submitted as an assignment without the necessary changes. If this practice is discovered, credit may be denied or revoked.
 

Business Statistics, 3e, by Robert A Donnelly, 2020, Pearson Education

Statistical Techniques in Business and Economics, Douglas Lind, William Marchal, and Samuel Wathen, 18e, 2020, McGraw Hill.
Statistics for Business and Economics by Paul Newbold, William L. Carlson, and Betty Thorne (Tenth Global edition).
Introduction to Statistics by Dr. Lauren Perry
https://bookdown.org/lgpperry/introstats/

【Pre-Learning】Students will be provided with lecture notes and handouts beforehand. Please read them before the class.

【Post-Learning】Lecture notes and handouts will contain exercises or questions. Please review them after the class.

Students will be asked to share their understanding of given concepts or asked to report orally what they discussed in groups.

Some concepts will be demonstrated using R, and we will discuss the output.

Engagement in classroom activities (20%)
Assignments (20%)  
Mid-term Evaluation (30%)
Final evaluation (30%)

In the classroom

This course is useful for any student interested in applying quantitative tools and techniques for data analysis.

Regular attendance is required.

N/A

Remember that making mistakes is a natural part of the learning process. Don’t fear mistakes; instead, view them as valuable opportunities for growth. Be proactive in your learning by seeking help, asking questions, and sharing your thoughts. Learning is a collaborative effort, and I’m here to support you every step of the way. Just as you are learning, so am I; together, we can create a positive and enriching experience. Let’s engage, explore, and learn from each other!