Quantitative and analytical techniques for managers

This module provides an introduction to the use of quantitative and analytical techniques in management research. It deals with the formulation of research hypothesis and the application of statistical methods to test hypotheses. The course covers, basic probability theory, hypothesis testing, bivariate analysis, linear regression, multiple linear regression, autocorrelation and heteroskedasticity, non-linear regression and regression models for binary and qualitative data. The course includes practical sessions using statistical packages.

Objectives and learning outcomes of the module

At the end of this module students should be able to:

• Demonstrate knowledge of hypothesis formulation and specification;
• Understand basic probability theory and its application to econometrics;
• Apply and interpret the classical linear regression model to time series and cross section data;
• Understand the assumptions of the classical linear model and the effects of violation of the assumptions;
• Apply diagnostic tests;
• Understand the problems of multicollinearity; heteroscedasticity and autocorrelation;
• Use and interpret models/techniques that correct for autocorrelation, heteroscedasticity and multicollinearity;
• Use and interpret regression models for qualitative and binary data;
• Understand the distinction between correlation and causation and of the problem of simultaneity;
• Show an appreciation of the limitations of quantitative analysis applied to complex systems of relationships that characterise many management and economic problems.

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