The course is conducted in English。Students in English-Taught Program in Department of Political Science have priority in taking this course.

The purpose of this course is to introduce the basic concepts of social statistics. This course mainly covers probability, random variables, probability distribution, and random samples. This will help students prepare for subsequent courses on statistical inference and basic regression models. The application of concepts is covered in this course, as well as mathematical reasoning, which will aid in conceptual comprehension. The relevant mathematical tools will be introduced in appropriate lecture topics. For example, the integral is an important tool for understanding continuous probability distributions. The syllabus will be adjusted to reflect actual progress.

This course serves as a foundation for future courses in statistical inference and quantitative analysis, enabling students to analyze social science issues in a more scientific manner. Learning Outcomes： Through a series of lectures, assignments and exams, students can,  Understand the fundamental concepts and mathematical reasoning of social statistics, particularly, the logic behind it  Combine concepts with practical applications, particularly, real-world cases

 Practicum Assignments: 10 assignments will be distributed during our practicum session and are due at midnight on the day preceding the practicum session scheduled for the following week. Each assignment usually contains two or three problem sets. The TA will be responsible for assignment design and discussing these problem sets during our practicum session  Mid-term exam: There will be an in-class mid-term exam that contains probability, random variables, discrete distributions, and continuous distributions. 60% of questions are of fundamental difficulty, 30% are of intermediate difficulty, and 10% are considered challenging  Final exam: There will be an in-class final exam that contains bivariate distributions, multivariate distributions, random samples and asymptotic theory. 70% of questions are of fundamental difficulty, 25% are of intermediate difficulty, and 5% are considered challenging. Grading： Practicum Assignments (50%) Mid-term exam (30%) Final exam (20%)

The following books are suitable for students with solid knowledge of mathematics DeGroot, Morris H. and Mark J. Schervish. 2012. Probability and Statistics. 4th ed. Pearson. Hogg, Robert V. and Elliot A. Tanis. 2014. Probability and Statistical Inference. 8th ed. Pearson. (Hogg, Robert V., Tanis, Elliot A. and Dale L. Zimmerman. 2024. Probability and Statistical Inference. 10th ed. Pearson) Students with a basic knowledge of mathematics can refer to the following book Weiss, Neil. 2017. Introductory Statistics. 10th ed. Pearson.

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