The course aims to provide students with important skills which are of both academic and vocational value, being an essential part of the intellectual training of an economist and social scientist and also useful for a career.
By the end of the module students should have competencies in the following: An awareness of the empirical approach to economic and social science; review and extend fundamental statistical concepts; methods of data collection and analysis; regression analysis, its extensions and applications; use of spreadsheets and statistical packages such as SPSS (Or STATA).
The module will typically cover the following topics:
Review of random variables, associated distributions and moments; review of statistical estimation, estimator sampling distributions and population inference; causality and selection bias; experimental versus non-experimental data; simple linear regression (SLR) model, assumptions, interpretation and hypothesis testing; multiple linear regression (MLR) model, assumptions, interpretation and hypothesis testing; modelling non-linear relationships; dummy variables; interaction terms; the failure of MLR assumptions; tests and implications for hypothesis testing; problems of endogeneity; instrumental variables; short panel data methods; Stata.
These courses are to:
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