Statistics 2 University of London

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Route map to the guide

This subject guide provides you with a framework for covering the syllabus of the ST104b Statistics 2 half course and directs you to additional resources such as readings and the virtual learning environment (VLE).
The following 10 chapters will cover important aspects of elementary statistical theory, upon which many applications in
EC2020 Elements of econometrics draw heavily. The chapters are not a series of self-contained topics, rather they build on each other sequentially. As such, you are strongly advised to follow the subject guide in chapter order. There is little point in rushing past material which you have only partially understood in order to reach the final chapter. Once you have completed your work on all of the chapters, you will be ready for examination revision. A good place to start is the sample examination paper which you will find at the end of the subject guide.
ST104b Statistics 2 extends the work of ST104a Statistics 1 and 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 and properties of these distributions are investigated.
Point estimation techniques are discussed including method of moments, least squares and maximum likelihood estimation. Confidence interval construction and statistical hypothesis testing follow. Analysis of variance and a treatment of linear regression models, featuring the interpretation of computer-generated regression output and implications for prediction, round off the course.
Collectively, these topics provide a solid training in statistical analysis. As such, ST104b Statistics 2 is of considerable value to those intending to pursue further study in statistics, econometrics and/or empirical economics. Indeed, the quantitative skills developed in the subject guide are readily applicable to all fields involving real data analysis.