ÐÓ°ÉÂÛ̳

The course provides a precise and accurate treatment of probability, distribution theory and statistical inference.

As such there will be a strong emphasis on mathematical statistics as important discrete and continuous probability distributions are covered (such as the Binomial, Poisson, Uniform, Exponential and Normal distributions). Properties of these distributions will be investigated including use of the moment generating function.

Point estimation techniques are discussed including method of moments, maximum likelihood and least squares estimation. Statistical hypothesis testing and confidence interval construction follow, along with non-parametric and goodness-of-fit tests and contingency tables. A treatment of linear regression models, featuring the interpretation of computer-generated regression output and implications for prediction, rounds off the course.

Prerequisites: No previous knowledge of statistics will be assumed, although familiarity with elementary statistics to the level of ME116 would be an advantage (for example, descriptive statistics – sample mean and variance). Mathematics to A-level standard or equivalent is highly desirable, i.e. competency with basic calculus, integration and algebraic manipulation (although a refresher document will be provided).

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)

Please note: Assessment is optional but may be required for credit by your home institution. Your home institution will be able to advise how you can meet their credit requirements. For more information on exams and credit, read Teaching and assessment

Collectively, these topics provide solid training in statistical analysis. As such, this course would be of value to those intending to pursue further study or a career in statistics, econometrics and/or empirical economics. The quantitative skills developed by the course are readily applicable to all fields involving data analysis.

• To provide a solid understanding of distribution theory which can be drawn upon when developing appropriate statistical tests. Useful properties of some important distributions will be reviewed as well as parameter estimation techniques for various probability distributions
• To facilitate a comprehensive understanding of the main branches of statistical inference, and to develop the ability to formulate the hypothesis of interest, derive the necessary tools to test this hypothesis and interpret the results
• To introduce the fundamental concepts of statistical modelling, with an emphasis on linear regression models with multiple explanatory variables

The design of this course is guided by ÐÓ°ÉÂÛ̳ faculty, as well as industry experts, who will share their experience and in-depth knowledge with you throughout the course.