Course Overview

This course is a formal introduction to Statistics. No prior knowledge of probability and statistics is required as all concepts will be developed from the ground up. We will cover a range of topics including descriptive statistics, basics of probability, random variables, distribution and density functions, sampling distributions, point estimation, confidence intervals, and hypothesis testing. If time allows, a preview of the regression analysis will be provided. The details of regression analysis will be covered in Introductory Econometrics, which is a continuation of this course.

Learning Achievement

Students are expected to understand fundamental concepts of statistical inference and to apply statistical methods to simple exercises.

Competence

This course is a formal introduction to Statistics. No prior knowledge of probability and statistics is required. We will cover a range of topics including descriptive statistics, basics of probability, random variables, distribution and density functions, sampling distributions, point estimation, confidence intervals, and hypothesis testing. Regression analysis will be covered in Introductory Econometrics, which is a continuation of this course.

1. General-propose competence: critical and creative thinking skills, data and information literacy.
2. Special competence: analytical skills on international relations, analytical skills on international development.

Course prerequisites

No Prerequisites

Grading Philosophy

Assignments(30%), Midterm Exam(30%) and Final Exam(40%)
Note that during the COVID-19 pandemic, the grading scheme is assignments (50%) and a take-home final exam (50%)

Course schedule

The course usually contains 9 lectures and 2 exams. Note that during the COVID-19 pandemic, we have 10 lectures and 1 take-home exam instead.
Introduction and descriptive statistics
Basics of probability
Random variables, cumulative distribution functions, discrete random variables
Continuous random variables, probability density function, Normal distribution
Two random variables and the joint distribution, covariance and correlation
Midterm exam
Chi-square distribution, Student's t distribution, F distribution, distributions of the sample mean and variance for normal populations. Large samples: law of large numbers and central limit theorem
Point estimation
Confidence intervals
Hypothesis testing
Not applicable

Course type

Lectures

Online Course Requirement

Instructor

YU ZHENGFEI

Other information

1. This course is taught online with asynchronous lecture sessions and synchronous Q&A sessions.

2. Regression analysis will be covered in Introductory Econometrics, which is a continuation of this course.

Site for Inquiry

Link to the syllabus provided by the university