Statistics 1 Part 2 is a self-paced course from LSE which aims to introduce you to and develop your understanding of essential statistical concepts, methods and techniques, emphasising the applications of these methods. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals or the LSE MicroBachelors program in Mathematics and Statistics Fundamentals.
Part 2, Statistical Methods, covers the following topics:
● Hypothesis testing I
● Hypothesis testing II
● Contingency tables and the chi-squared test
● Sampling design and some ideas underlying causation
● Correlation and linear regression
Statistics 1 Part 2 forms part of a series of courses which focuses on the application of statistical methods in management, economics and the social sciences. Attention will focus on how to approach statistical problems, as well as the interpretation of tables and results.
This course is part of the Mathematics and Statistics Fundamentals MicroBachelors Program and the Statistics Fundamentals MicroBachelors Program.
Statistics 1 Part 2 assumes no prior knowledge of statistics. Although there are no formal prerequisites for this course, it is strongly recommended to study the LSE statistics courses in order, given the cumulative nature of the subject matter. Ideas of probability, inference and multivariate analysis are introduced and are further built on in Statistics 2 Parts 1 and 2.
What you'll learn
By the end of this course, you will:
- be familiar with some further key ideas of statistics that are accessible to a student with a moderate mathematical competence
- be able to routinely apply a variety of methods for explaining, summarising and presenting data and interpreting results clearly using appropriate diagrams, titles and labels when required
- To explain the fundamentals of statistical inference and perform inference to test the significance of common measures such as means and proportions and conduct chi-squared tests of contingency tables
- be able to use simple linear regression and correlation analysis and know when it is appropriate to do so
- Hypothesis testing I
- Hypothesis testing II
- Contingency tables and the chi-squared test
- Sampling design and some ideas underlying causation
- Correlation and linear regression