Bram Driesen


Quantitative Methods II

QM II continues the quantitative topics that were initiated in QM I: mathematics and statistics. There is no separate formal training in (or testing of) computer science: this element has been integrated into the remaining two parts of the course. In the mathematics part, we will expand the analysis of functions and (systems of) equations. Issues that will be addressed are: - A collection of tools often used in finance but also in other fields (buzzwords: interest rates, present value, discounting, and geometric series). - The matrix representation of systems of linear equations (so called linear algebra) will be introduced and supplemented by the concepts of determinants and inverse matrices, which are important tools to manipulate such systems. All these topics will be introduced and illustrated using economic or business applications. In the second half of the course, we introduce the mathematical programming approach to solving decision problems in business. The analysis will focus on the variety of business decision problems that can be modeled as linear programming models. The emphasis is on modeling, while finding the optimal solution is left to the computer. In the statistics part, we will expand the coverage of inferential statistics, i.e. how to draw conclusions about a population based on a sample. Students will learn to apply the basic tools of inferential statistics (confidence intervals and hypothesis tests) to examine a large array of questions that may occur in economics or business. We will focus on the following topics: -How to examine whether the mean of some quantitative variable (e.g. income) differs between two or more populations (e.g. men vs. women). Related to this, we will also examine what to do when the data are paired, and when the variable of interest is a proportion. -How to analyse relationships between qualitative variables (e.g. between brand preference and gender). -How to analyse relationships between two or more quantitative variables (e.g. between income and age) using regression analysis. This is one of the most frequently used statistical techniques in economics and business. All these issues will involve the use of real-life data, which will be analysed using EXCEL.