Prerequisites: No previous knowledge of statistics will be assumed, although familiarity with elementary statistics to the level of EC112 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).
Dr James Abdey
Course Structure: Course content will be delivered by formal lectures supported by daily classes. All topics will be explained during lectures accompanied by examples demonstrating the material. A comprehensive course pack will be provided and daily exercise sets will be distributed to provide an opportunity to practice problems. Solutions to exercises will be discussed and distributed in the classes. Additional self-study exercises and solutions will also be made available online.
Course Objectives: 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.
Collectively, these topics provide a solid training in statistical analysis. As such, this course would be of value to those intending to pursue further study in statistics, econometrics and/or empirical economics. Indeed, the quantitative skills developed by the course are readily applicable to all fields involving real data analysis.
Texts: As stand-alone resources will be provided, there will be no need to rely on a particular text. There are several good texts at the right level for this course which can be used in support of the course materials, including:
Freedman, D., Pisani, R. and R. Purves (2007) Statistics, Norton, 4th edition.
Larsen, R.J. and M.J. Marx (2011) An Introduction to Mathematical Statistics and Its Applications, Pearson Education, 5th edition.
Lectures: 36 hours Classes: 12 hours
Assessment: Two written examinations