Dr Sara Geneletti Inchauste

Dr Sara Geneletti

This course is compulsory on the BSc in Business Mathematics and Statistics, BSc in Data Science and BSc in Mathematics, Statistics and Business. This course is available on the BSc in Politics and Data Science. This course is available as an outside option to students on other programmes where regulations permit. This course is not available to General Course students.

Specifically the course is available to Accounting and Finance students who have taken ST102.

This course cannot be taken with ST201 Statistical Models and Data Analysis.

ST102

Students who have no previous experience in R are required to complete an online pre-sessional R course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=7745)

Students must have completed one of the following two combinations of courses: (a) ST102, or (b) ST109 and EC1C1. Equivalent combinations may be accepted at the lecturer’s discretion.”

Statistical data analysis in R covering the following topics: Simple and multiple linear regression, Model diagnostics, Detection of outliers, Multicollinearity, Introduction to GLMs

This course will be delivered through a combination of classes and lectures (both or either of which maybe held online) totalling a minimum of 30 hours across Lent Term. This course includes a reading week in Week 6 of Lent Term in which a the students work independently on a mini-project (no lectures).

Regular Moodle quizzes. .

1. Gelman and Hill, Data analysis Using Regression and Multilevel/Hierarchical models (CUP, 2007) First part.

2. Neter, J., Kutner, M., Nachtsheim, C. and Wasserman, W. Applied Linear Statistical Models, McGraw-Hill, Fourth Edition. (2004).

3. Abraham, B. Ledolter, J. Introduction to Regression Modelling, Thomson Brooks Cole. (2006).

4. S. Weisberg Applied Linear Regression, Wiley, 3rd edition. (2005)(intermediate).

5. Fox (2016) Applied Regression Analysis and Generalized Linear Models.

Project (55%) and project (35%) in the ST Week 2.
Coursework (10%) in the LT Week 6.

10%: A group work mini-project to be handed in at the end of reading week (LT week 8)

55%: A group work multiple linear regression project to be handed in by the second week of the ST

35%: An individual logistic regression project to be handed in at the same time as the group project in the second week of the ST.