Course Description
This course introduces the fundamental concepts of statistics and their application in the business world. Students will learn to collect, analyze, interpret, and present data in a business context. The course emphasizes the use of statistical software to perform analyses and solve problems encountered in various business disciplines, including marketing, finance, operations, and human resources. Through lectures, hands-on exercises, and case studies, students will gain the skills needed to apply statistical reasoning to inform strategic decision-making.

Course Objectives
Upon successful completion of this course, students will be able to:
1. Understand and apply basic statistical terminology and concepts in a business context.
2. Analyze and interpret data using descriptive and inferential statistics.
3. Make informed decisions based on statistical analysis.
4. Use statistical software to perform data analysis.
5. Present statistical findings effectively to a business audience.
Key Topics:
• Introduction to Statistics in Business: role and importance in decision-making.
• Data Collection and Sampling Methods: techniques for gathering and selecting representative data.
• Descriptive Statistics: measures of central tendency, variability, and data visualization techniques.
• Probability Concepts: understanding uncertainty and risk in business decisions.
• Inferential Statistics: hypothesis testing, confidence intervals, and their application in business.
• Regression Analysis: exploring relationships between variables for prediction and forecasting.
• Time Series Analysis: analyzing trends and patterns over time.

Assessment:
• Midterm Examination: 20%
• Final Examination: 30%
• Four Homework Assignments: 30%
• Five In-Class Quizzes: 20% (you can drop your lowest quiz score)

Late work:
Homework assignments are due on midnight(KST) of the scheduled due date (please check the course schedule below)
• Less than 24 hours overdue: 10% points deduction
• Less than 48 hours overdue: 50% points deduction
• More than 48 hours overdue: No credit

You are not required to buy a textbook. All lecture slides and other necessary class materials will be posted on the class webpage. However, if you are new to statistics, textbook would be helpful.
Keller, G. (2022). Statistics for Management and Economics. Cengage Learning. ISBN: 9780357714270.
https://www.cengage.com/c/statistics-for-management-and-economics-12e-keller/9780357714270/.

Important: class topics are subject to change, contingent on mitigating circumstances and the progress we make as a class. Students are encouraged to attend online lectures and check the course website for updates.

Week / Date / Topic / Homework(due) / Quiz
1 / 2024-02-22 / Central Tendency Measures
2 / 2024-02-29 / Exploratory Data Analysis
3 / 2024-03-07 / Probability & Sampling Distributions 1
4 / 2024-03-14 / Probability & Sampling Distributions 2 / Quiz 1
5 / 2024-03-21 / Hypothesis Testing
6 / 2024-03-28 / Independent Group t-Test / Quiz 2
7 / 2024-04-04 / Effect Size, P-Value, Power / HW1
8 / 2024-04-11 / Mid-Term Exam
9 / 2024-04-18 / Analysis of Variance / Quiz 3
10 / 2024-04-25 / Chi-Square Test for Independence / HW2
11 / 2024-05-02 / Linear Regression 1
12 / 2024-05-09 / Linear Regression 2 / HW3 / Quiz 4
13 / 2024-05-16 / Multiple Regression
14 / 2024-05-23 / Nonparametric Statistics / HW4
15 / 2024-05-30 /Time-Series Analysis / Quiz 5
16 / 2024-06-06 / Final Exam

Course format
This course will take place remotely; there will be no in-person meeting. However, the class will be fully synchronous; classes will take place 11:00-14:00 Korea Standard Time(UTC+9) on Thursdays via Zoom. The recorded lectures will be uploaded on the course website(PLMS)

Software
• During the course, we will be doing some hands-on exercises with Jamovi software (https://www.jamovi.org/)

Academic Dishonesty Policy
It is essential to uphold the highest standards of academic integrity in all aspects of this course. Each student is expected to submit original work for every assignment. If it is determined that a student has engaged in plagiarism, which includes copying the work of another student, no credit will be awarded for the submitted assignment. Further disciplinary action may be taken in accordance with the institution’s academic integrity policy. This measure ensures fairness and encourages genuine learning and intellectual development. This statement not only addresses the specific issue of copying work but also situates it within the broader context of academic integrity, offering a clear rationale for the policy.