EdX

Statistics 2 Part 2: Statistical Inference (edX)

Offered by The London School of Economics and Political Science, LSE,
Statistics 2 Part 2: Statistical Inference (edX)

The final part 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 2 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 and Statistics 2, Part 1. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals.
Part 2, Statistical Inference, covers the following topics:

  • Sampling distributions of statistics
  • Point estimation I
  • Point estimation II and interval estimation
  • Hypothesis testing
  • Analysis of variance (ANOVA)

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 courses in statistics and econometrics are introduced.
This course is part of the Statistics Fundamentals MicroBachelors Program.

Prerequisites:
Statistics 2 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. Statistics 1, Parts 1 and 2, as well as Statistics 2, Part 1, 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
  • 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
  • explain the fundamentals of statistical inference and apply these principles to justify the use of an appropriate model and perform tests in a number of different settings
  • demonstrate understanding that statistical techniques are based on assumptions and the plausibility of such assumptions must be investigated when analysing real problems.

Syllabus

  • Sampling distributions of statistics
  • Point estimation I
  • Point estimation II and interval estimation
  • Hypothesis testing
  • Analysis of variance (ANOVA)
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