96844 - STATISTICS

Learning outcomes

At the end of the module, students: - know the most relevant univariate and multivariate analytical techniques and tools; - can perform statistical analyses of cross-sector and longitudinal data and deliver suitable graphic representations, making results comprehensible to organizations’ decision makers.

Course contents

1. Introduction: Principles of Risk and Insurance; Portfolio Theory.

2. Questions, Theories, Hypothesis Testing

3. Measurement and Summarization

4. Statistical Inference

5. Regression Analysis

6. Multiple Regression Analysis

7. Statistical Analysis of Time-Series and Panel Datasets

8. Applied Statistical Studies and the Law

Readings/Bibliography

Wooldridge JM (2016) Introductory Econometrics, 6^th (International) edn, South-Western Cengage Learning: Mason (OH).

Teaching methods

The course includes lectures and essential readings (marked with an asterisk (*), above) to introduce the key concepts, supplemented by the textbook chapter readings and supplementary reading in the form of journal papers.

Apart from the first lecture, each subsequent lecture is accompanied by statistical laboratory work. The laboratory work requires class members to analyse and interpret real datasets (which will be supplied for this purpose) using specialist statistical software (i.e., Stata), with the support of Professor Connelly who will demonstrate these methods in the statistical laboratory and in lectures.

In addition to the required readings, a range of additional resources (e.g., links to resources on the web) will also be supplied to assist class members, especially for further guidance in the use of the specialist statistical software.

Note: The emphasis of this course is applied data analysis, on understanding the results of statistical analysis, and on presenting the results in a meaningful way to an educated audience (of non-statisticians). Students will be required to analyse and present data and to explain the results, in words. (Students will not be required, for example, produce formulae, theorems, etc. or to conduct analyses using statistical software under exam conditions.)

Assessment methods

After the fourth laboratory session, students will be required to (i) submit a log of at least three of their laboratory work sessions (15% of the course weight); and (ii) to submit a short (<5-page) written analysis of at least one empirical/applied project (40% of the course weight) before the final examination; and to sit a final, open-book, examination of 2 hours’ duration (45% of the course weight) at a date to be fixed.

Examples of the type of log that is required for (i) and the type of report that is required for (ii) will be supplied to the class in the first week of lectures.

The final examination is an open-book exam.

Teaching tools

Extensive materials including lecture slides and additional learning files, datasets, logs and practice exams (as well as real, previous exams) will be available on the Virtuale site for this course.

Office hours

See the website of Luke Brian Connelly