Master basic statistical concepts and methods

Analysis

Develop data analysis and decision-making skills

Enquiry & Analysis

Applying statistical tools to solve practical problems

Knowledge & Understanding; Learning, Application

Understand the application of statistics in different fields

Application

Attendance Rate - Calculate attendance times

Mid-term Exam 10% - Mid-term exam calculated based on the score on the paper

Class Tests 10% - The main assessment is the degree to which students learn, review, understand, and master the knowledge points of each class.

Classroom Communication and Defence 10% - Classroom questioning and answering

Homework 10% - Grade will be given based on completion and submission of homework outside of class

Final Exam 50% - The exam grade will be included in the total module grade. The examination questions are single choice questions, multiple-choice, short answer, calculation, comprehensive analysis, etc. Among them, 20% of the questions correspond to LO 1, 30% correspond to LO 2, 30% correspond to LO 3, and 20% correspond to LO 4.

This module aims to cultivate students' core abilities in data analysis and decision-making through a teaching approach that combines theory with practical cases. The module content covers key topics such as descriptive statistics, probability distribution, sampling and estimation, hypothesis testing, and regression analysis. Students will learn how to organize, present, and analyse data, understand probability distribution and its applications, master inference methods from sample to population, learn to scientifically construct hypotheses and conduct hypothesis testing, and establish regression models to explain and predict the relationships between variables. Through these studies, students will not only acquire skills in data analysis, but more importantly, cultivate critical thinking, statistical thinking, and teamwork abilities, enabling them to better cope with complex problems in future learning and careers, provide a scientific basis for decision-making, and contribute to the development of various industries. At the same time, it also lays the foundation for learning econometrics, management accounting, securities analysis technology, insurance actuarial science and especially other branch modules in the senior year of statistics. This includes:



Data and Statistics; Description of Statistical Data; Probability, Probability Distribution, and Sampling Distribution; Parameter Estimation; Hypothesis Testing; Analysis of Variance and Experimental Design; Correlation and Regression Analysis; Time Series Analysis and Prediction; Statistical Index; Basic Knowledge of National Economic Statistics

Through the study of this module, students will be able to better understand the talent development goals and graduation requirements and apply the data analysis skills and decision-making abilities covered by statistics to their respective professional fields. Whether in the fields of economics, sociology, natural sciences, or engineering, statistics, as an interdisciplinary field, provides students with powerful tools to discover patterns, explain phenomena, and support decision-making from data. By cultivating statistical skills, students can better adapt to the constantly changing social and professional environment, laying a solid foundation for their future learning and work.

The learning strategy for the module requires students to commit 150 learning hours (including assessment) of which there will be 36 hours of tutor-led learning and 114 hours of independent and self-directed study.



Case analysis: Provide real cases to enable students to apply statistical methods to practical situations, helping them understand the role of statistics in problem-solving. Practical operation: Provide datasets for students to personally operate statistical software for data analysis and cultivate their practical operational abilities. Interactive discussion: Guide students to participate in discussions, solve problems together, share different perspectives and methods, and cultivate critical thinking and collaborative abilities. Visualization tools: Using visualization tools such as charts and images to visualize abstract concepts can help students better understand and remember them.

(1) Textbook

Edited by Yuan Wei, Pang Hao, et al., Statistics (5th edition), Higher Education Press

(2) Reference list (or required reading list)

Edited by Jia Junping, He Xiaoqun, and Jin Yongjin, Statistics (7th edition), Renmin University of China Press, January 2018

Edited by Chen Ping and Gan Xiaowen, Fundamentals of Statistics, Jiangxi University Press, December 2013