Course description
Statistics 2 Part 2: Statistical Inference
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.
Upcoming start dates
Who should attend?
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.
Training content
- Sampling distributions of statistics
- Point estimation I
- Point estimation II and interval estimation
- Hypothesis testing
- Analysis of variance (ANOVA)
Course delivery details
This course is offered through The London School of Economics and Political Science, a partner institute of EdX.
12-15 hours per week
Costs
- Verified Track -$199.2
- Audit Track - Free
Certification / Credits
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.
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