SHE Level 1
SCQF Credit Points 20.00
ECTS Credit Points 10.00
Module Code M1L125512
Module Leader Athanasios Tsekeris
School Glasgow School for Business and Society
Subject Economics
  • B (January start)

Summary of Content

This module will provide students with an introduction to the concepts, methods and techniques used in quantitative analysis and necessary for financial decision-making and problem solving. It will equip them with a variety of technical and analytical skills that can underpin their programme of studies and future careers. The module also aims to improve students' numeracy skills by building up their confidence in the use of mathematical techniques. The use of quantitative tools is taught within the context of the financial decision-making processes of firms in various industry segments and using available technologies such as mobile phone apps, calculators, spreadsheets (MS Excel), and statistical software (SPSS). The emphasis will be on financial problem solving applied to individuals, business, and the public sector, nationally and globally, and set in a range of contexts including personal finance, new business and social enterprise development. Summary of how PRME-related issues / topics are covered in this module: Purpose: this module will develop the students' capabilities to better understand, through statistics and data analysis, current issues in business and society at large, and to start thinking about the complexity of planning and implementing sustainable and lasting solutions. Values: the critical understanding of data sources, methods of analysis, and description of findings will help students to discern potential issues with reported statistics and thus promote the ethical use of quantitative information Method: through the use of databases and reported statistics the students will be exposed to the importance of the analysis and presentation of information to support decision-making Research: this module introduces students to fundamental quantitative techniques used in research and applied through learning experiences that deal with the social, environmental and economic impact of financial decision-making


Weeks 1 and 2: Data Collection and Presentation Data applications in Business and Management Data definition: Elements, Variables and Observations Discrete and continuous data. Types of data Frequency, e.g. annual, monthly, quarterly. Scales of Measurement: Nominal Scale Interval Scale Ordinal Scale Ratio Scale Data tabulation. Methods of data presentation: pie charts, bar charts histogram etc. Types of datasets: panel, time series and cross-sectional data sets. Week 3: Summarising Data Measures of central tendency and application: Summarising data for a discrete variable Summarising data for a continuous variable Summarising data for two variables using tables: Cross tabulation Summarising data for two variables using graphical displays: Scatter diagram, Trend line, side-by-side charts etc. Measures of central tendency: Mean Weighted Mean Median Mode Percentiles and Quartiles Measures of dispersion: Range Interquartile Range Variance Standard Deviation Measuring shapes of distributions: Skewed distribution and Bell-Shaped distribution Weeks 4 and 5: The Concept of Probability and Probability Distributions The concept of probability. Simple and General laws of probability: Additional Law Multiplication Law Discrete Probability distributions: Basic binomial probability. Expected value and Variance : Continuous Probability distributions: Normal probability distribution curve Week 6 and 7: Understanding Relationships in Data Analysis Correlations analysis, Cross tabulation Interpretation of the Correlation Coefficient Simple Hypothesis testing: students' t-test critical values Confidence Intervals Weeks 8 and 9 Introduction to Linear Forecasting Introduction to Simple Linear Regression Regression model and regression equation Estimated multiple regression equation Scatter diagrams. Ordinary least squares regression (OLS) Model Assumptions Interpretation of the results Multiple Regression Regression model and regression equation Estimated multiple regression equation Model Assumptions Testing for significance Interpretation of the results Week 10: Study week Week 11: Management decision-making under uncertainty Decisions problems Maximax, Maximin, Minimax Regret Decision using probability and decision trees Week 12: Class Revision Lecture

Learning Outcomes

On successful completion of this module, the student should be able to:1. Explain the role of data gathering, analysis, and interpretation in financial decision-making2. Use statistical software (SPSS) and MS Excel competently to process quantitative information3. Identify contextual issues that may affect the choice of quantitative analysis tools4. Discuss the validity of a range of commonly used forms of quantitative information in financial decision-making5. Develop arguments supported by quantitative information6. Apply a range of commonly used mathematical techniques to inform financial decisions

Teaching / Learning Strategy

Students will have 24 hours of lectures, 24-hours of tutorials, and 152 hours of self-directed learning and assessment. Lectures will introduce key concepts and techniques that will be followed-up by applied exercises in the labs, during the lab sessions where MS Excel and statistical software (SPSS) will be used

Indicative Reading

Recommended main textbook Norean R. Sharpe, Richard D. De Veaux, Paul Velleman (2017); Business Statistics, Global Edition, 3/E; Pearson; ISBN-10: 1292243724; SBN-13: 9781292243726; Pearson Other useful textbooks: Mik Wisniewski (2016); Quantitative Methods for Decision Makers, 6/E; Pearson; ISBN-10: 0273770683; ISBN-13: 9780273770688 Robert A Donnelly (2015); Business Statistics, 2nd Ed; Pearson; ISBN-10: 0321925122; ISBN-13: 9780321925121 [RP1] James T. McClave, P. George Benson, Terry Sincich (2015); Statistics for Business and Economics; Pearson; ISBN-10: 1292227087; ISBN-13: 9781292227085 Online sources: Statistical Data and Reports UK Office for National Statistics European Statistics United States Census Bureau (National Statistics) National Bureau of Statistics of China Websites Bloomberg Euromoney Reuters IMF The World Bank Bank for International Settlements (BIS) Bank of England US Federal Reserve European Central Bank

Transferrable Skills

By the end of this module students will have gained competence in the following key areas[RP1] : Numeracy: confidently select and apply the appropriate quantitative analysis techniques to analyse and evaluate financial problems IT: demonstrate competence in the application of MS Excel and SPSS applications Project management: manage independent learning tasks and timelines to deliver required assignments to achieve learning goals and objectives Teamwork: engage in group assignments and adapt effectively to different roles and processes Communicate effectively, orally and in writing, by selecting a format and style appropriate to the context

Module Structure

Activity Total Hours
Independent Learning (FT) 122.00
Tutorials (FT) 24.00
Assessment (FT) 30.00
Lectures (FT) 24.00

Assessment Methods

Component Duration Weighting Threshold Description
Exam 02 1.00 40.00 35% MCQ class test in Week 12 on concepts and ideas
Exam 01 n/a 60.00 35% 4 Class tests (related to tasks) to be taken during Lab sessions ( weeks 3, 5, 7, 9)