This module aims to provide you with an opportunity to develop an appreciation of management research in theory and practice. There are two broad objectives: (1) to enhance your knowledge of the research process and enable you to be aware of the problems associated with research, and (2) to prepare you to carry out your own research, in most cases your dissertation.
It is important that we provide bioinformatic cell analysis training to students to significantly improve research possibilities in their future careers in Biomedical Sciences. The quantitative cell biology (QCB) module will focus on the practical use of the methods employed, rather than focussing on just the mathematics and statistical approaches underpinning them. Some of the mathematics and statistics will be discussed, but no prior knowledge will be assumed. The analyses will predominantly be conducted using the R project for statistical computing software (https://www.r-project.org). Students with or without experience of R programming and/or mathematics will be enrolled on this course. Students with no background in this area will not be disadvantaged, as they will be provided with computing support, and training via attendance on a data carpentry course delivered by the Southampton Research Software Group (https://rsgsoton.net), to succeed. There is no opportunity to repeat the year on this programme.
This module familiarises students with the main empirical methodologies used in addressing economic question and in analysing and evaluating economic policy. Econometric methods will presented and applied to actual economic issues, including using appropriate statistical software.
The purpose of this module is to provide you with the necessary skills to undertake quantitative research in finance. In particular, we focus on analysing financial markets and firms’ investment and financing decisions. Lectures will introduce a broad range of topics (e.g. ARCH/GARCH). However, you will discover that by understanding and applying some basic concepts various issues can be analysed in a similar manner. In particular, we will introduce basic theoretical concepts developed in statistics and econometrics. Understanding the main theoretical methods is essential to appreciate the analytical tools and their applications to finance. The module is a compulsory module on the MSc Finance. The module introduces empirical methods used in finance and is a prerequisite for Advanced Time Series Modelling in the 2nd semester. In particular, cross-sectional, panel and time series methods are introduced and applied to financial data. The module will introduce methods developed in econometrics and apply these methods to financial data. The module will stress the relationship between finance, econometrics and statistics. The module will only be offered on the MSc Finance. The module provides an introduction to time series modelling, which will be extended in the optional module Advance Time Series Modelling (MANG6297).
Assessment in the module takes the form of an online software skills test (worth 10% of the final mark) and a final written exam (worth 90%).
MANG6003 aims to develop statistical reasoning. Via a series of examples and activities, students are introduced to the idea of probability modelling and how it can be applied to aid decision making in uncertain situations, which are frequently encountered in organisations. On successful completion of this module, students should be able to collect relevant data and summarise the main features of an uncertain situation, to identify standard problems and analyse them with the correct statistical tools, to process and analyse data in a statistical computer package, to understand the risks involved in a decision which involves uncertainty, and quantify such risks. Students should also develop problem solving skills, modelling skills, become familiar with a standard statistical computer package (SPSS), and be able to interpret and critically evaluate statistical results.
You will be introduced to a number of key statistical concepts and data presentation formats. Beginning with exposure to a variety of data types defining the nature and properties of data you are likely to encounter. Emphasis is placed on distinguishing between population parameters and sample statistics and exploring the nature of distributions. Aided via the introduction of R Studio, a dedicated statistical software, you will become familiar with the concept of central tendency and the measurement of variation, and how these may be presented graphically. Emphasis is placed on information transfer to aid presentations, essays, reports and dissertation. A significant portion of the unit is given to developing your understanding of a variety of common statistical procedures including establishing the presence and strength of a relationships and standard approaches for determining if significant differences exist between groups within a variety of experimental designs. Central to this is the concept of hypotheses testing.
The module introduces some widely used quantitative approaches for characterizing uncertainty and risks in finance and management problems. The aim of the module is to introduce a number of widely used techniques for uncertainty and risk management and provide an understanding of how they can be used in practice.
This module develops analytical skills required for the final year Honours Project, scientific research in general, and your future career. The major skills are computer literacy and graphical presentation, understanding of scientific method and hypothesis testing, a few simple mathematical concepts, and basic methods of statistical analysis, including non-parametric tests, analysis of variance and data modelling.
The module will provide an opportunity for students to use A-level mathematical skills in studying Economics, Econometrics, Actuarial Science, and Management Sciences throughout their degrees. Pre-requisite for ECON2041 One of the pre-requisites for MATH3063, MATH3085
This module provides you with the opportunity to engage with econometrics theory focusing, in particular, on analysing financial markets and firms' investment and financing decisions. The module will systematically prepare you with the necessary skills to undertake quantitative research using both advanced theoretical knowledge and implementations using econometric software.
This module introduces you to quantitative research methods within the social sciences. The module is aimed at providing a firm understanding of the fundamental principles of quantitative analysis up to bivariate analysis, and a good foundation of knowledge of quantitative methods and their application to data relevant to disciplines across the Social Sciences, particularly Gerontology. You will learn about the analysis and data manipulation of quantitative data through a combination of online lectures, online exercises using SPSS, assessed coursework, tutorials, and individual study and practice. The module assumes no prerequisite knowledge of quantitative analysis and SPSS.
This module offers a more advanced training in quantitative research methods within the social sciences. The module is aimed at providing a deeper understanding of the fundamental principles of quantitative analysis, and a solid foundation of knowledge of quantitative methods and their application to data relevant to disciplines across the Social Sciences, particularly Gerontology. You will learn about a variety of regression analysis methods through a combination of online lectures, online exercises using SPSS, assessed coursework, tutorials, and individual study and practice. The module assumes prerequisite knowledge of statistical inferences, bivariate analysis, and SPSS.
