Descriptive Statistics

The Descriptive Statistics course provides a basic understanding of statistical methods used to describe, analyze, and present data concisely and informatively. This course emphasizes the application of statistical concepts in business and economic contexts, such as data management, trend analysis, and data-driven decision-making.

The final learning objectives are for students to be able to (1) understand the basic concepts of descriptive statistics, including measuring central tendency, dispersion, and data presentation; (2) analyze and present data in effective tables, graphs, and diagrams; (3) use descriptive statistics to support decision-making in business and economic contexts; (4) apply statistical calculation techniques using software such as Excel or other statistical applications; and (5) interpret data analysis results to solve real-world problems in business and economics. The study materials covered are (1) Introduction to Statistics: Basic concepts and statistical terminology; (2) Data Collection: Sampling techniques, surveys, and experiments; (3) Data Presentation: Frequency tables, histograms, bar graphs, and pie charts; (4) Measurement of Central Tendency: Mean, median, and mode. (5) Dispersion Measurement: Variance, standard deviation, and interquartile range. (6) Data Distribution Analysis: Distribution shapes and outliers. Learning Methods and Strategies include (1) Interactive Lecture Method, which is an explanation of concepts accompanied by active discussions in class to help students understand the theory and basic concepts of descriptive statistics. (2) Case Studies include data analysis from real cases in business and economics to apply the theories learned to practical situations. (3) Independent Practice in the form of practice questions using real data with statistical software to hone students' technical skills. (4) Collaborative Learning in the form of group discussions to complete project assignments that involve collecting, analyzing, and interpreting data together. (5) Problem-Based Learning (PBL). In this method, students are given real problems in business or economics that are relevant to the topic of descriptive statistics. This problem becomes the starting point for independent and group learning. Students are asked to analyze the data or problematic situation, identify what needs to be learned to solve the problem, and then present solutions based on statistical analysis. The aim of this method is to encourage the development of critical thinking skills, collaboration, and data-based decision-making. Students learn not only from the material, but also from experience in solving real problems. Assessment methods used include (1) Written Exam: Testing understanding of concepts and data analysis skills. (2) Individual Assignments: Completing statistical problems with analysis of results. (3) Group Assignments: Data analysis projects based on case studies and PBL. (4) Presentations: Oral presentation of data analysis results to demonstrate data interpretation and communication skills. (5) Class Participation: Active participation in discussions and learning activities, including in solving PBL-based problems.