Essential Statistics for Economics and Econometrics

  • Summer schools
  • Department of Statistics
  • Application code SS-ME116
  • Starting 2019
  • Short course: Open
  • Location: London

This is an introductory course on probability and statistics, with examples to demonstrate their applications in the social and natural sciences. 

There will be a strong emphasis on the fundamental concepts of probability theory, random variables and their distributions, sampling theory, statistical inference, and linear regression. Inferential techniques such as estimation, hypothesis testing, and regression analysis are important in the fitting and interpretation of statistical models. We will explore the mathematical background of these concepts and techniques, and demonstrate their use through practical examples and interactive experiments. 

The course should be of value to anyone intending to pursue further study in statistics or any other field involving the analysis of data

Session: One
Dates: 17 June – 5 July 2019
Lecturer: Dr Miltiadis Mavrakakis


Programme details

Key facts

Level: 100 level. Read more information on levels in our FAQs

Fees:  Please see Fees and payments

Lectures: 36 hours 

Classes: 18 hours

Assessment*: Two written examinations

Typical credit**: 3-4 credits (US) 7.5 ECTS points (EU)

*Assessment is optional

**You will need to check with your home institution

For more information on exams and credit, read Teaching and assessment


No previous knowledge of statistics will be assumed, but the use of formulae and the ability to perform basic algebraic manipulations will be necessary. Some knowledge of basic calculus, although not necessary, would be an advantage.

Programme structure

  • Descriptive statistics
  • Probability theory
  • Distribution theory
  • Sampling
  • Point estimation
  • Confidence intervals
  • Significance tests
  • Correlation
  • Linear regression

Course outcomes

  • To provide essential knowledge of the theory and key properties of probability and random variables, and the application of these concepts in practical situations
  • To facilitate an understanding of the main branches of basic statistical inference, and to develop the ability to use statistical techniques to analyse data and assess the accuracy of the resulting estimates and conclusions
  • To introduce the fundamental concepts of statistical modelling, particularly linear regression models. This course should be of value to those intending to study any course involving economic modelling and econometrics, or any further course in statistics


Department of Statistics at LSE has a distinguished history. Its roots can be traced back to the appointment of Sir Arthur Lyon Bowley, an alumnus of the University of Cambridge, at LSE in 1895. He was appointed Chair in Statistics in 1919, probably the first appointment of its kind in Britain. The Department of Statistics was submitted jointly to REF 2014 with LSE's Department of Mathematics: 84% of the research outputs of the two departments were classed as either world-leading or internationally excellent in terms of originality, significance and rigour.

The department has an international reputation for development of statistical methodology that has grown from its long history of active contributions to research and teaching in statistics for the social sciences.

On this three week intensive programme, you will engage with and learn from full-time lecturers from the LSE’s statistics faculty.

Reading materials

There will be no need to rely on a particular textbook, as full notes will be provided. There are several good textbooks at the right level for this course, such as:

P. Newbold, Statistics for Business and Economics (6th edition or later), Prentice Hall (2007-). 

R.J. Larsen and M.L. Marx, An Introduction to Mathematical Statistics and Its Applications (3rd edition or later), Prentice Hall (2001-).

*A more detailed reading list will be supplied prior to the start of the programme

**Course content, faculty and dates may be subject to change without prior notice

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