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Statistics 2 Part 1: Probability and Distribution Theory (edX)

Offered by The London School of Economics and Political Science, LSE,
Statistics 2 Part 1: Probability and Distribution Theory (edX)

The third in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.

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Statistics 2 Part 1 is a self-paced course from LSE which aims to develop your knowledge of elementary statistical theory, particularly relating to the concepts, methods and techniques of measurement and hypothesis testing that were introduced in Statistics 1, Parts 1 and 2. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals.
Part 1, Probability and Distribution Theory, covers the following topics:

  • Probability theory I
  • Probability theory II
  • Random variables
  • Common distributions of random variables
  • Multivariate random variables

There is an emphasis on topics that relate to econometrics, finance and quantitative social science. Concepts and methods that provide the foundations for more specialised undergraduate-level courses in statistics and econometrics are introduced.
This course is part of the Statistics Fundamentals MicroBachelors Program.

Prerequisites:
Statistics 2 part 1 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. Statistics 1, Parts 1 and 2, provide a solid foundation for this course.

What you'll learn
By the end of this course, you will:

  • Have developed key ideas from Statistics 1 that are accessible to a student with a moderate mathematical competence
  • apply and be competent users of standard statistical operators
  • be able to recall a variety of well-known distributions and their respective moments

Syllabus

  • Probability theory I
  • Probability theory II
  • Random variables
  • Common distributions of random variables
  • Multivariate random variables
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