Data analysis for management decision making MN2196

This course develops a comprehensive framework to evaluate whether the predictions of managerial, social and economic theories are supported by empirical evidence.

This course is an introduction to the quantitative techniques guiding evidence-based managerial decision-making. For instance, are family-managed firms less productive than other firms? Are there biases in job interviews? Are political connections important for corporations? Does performance-pay increase productivity? This course provides the tools to investigate empirically whether these statements are supported by empirical evidence.


If you are studying for this course as part of a BSc degree, you must have already passed:

  • ST105A Statistics 1 (half course) and
  • MT105A Mathematics 1 (half course)

Topics covered

Main topics of the module include:

  • The Linear Regression Model: the causality problem, the population model, sampling processes, estimators and estimates, the ordinary least squares (OLS) model, assumptions and properties of the OLS model. Limitations of linear regression: omitted variable bias, non-random sampling, measurement error, outlying observations, heteroskedasticity.
  • Multiple Regression: omitted variable bias problem, the multiple regression model, assumptions and properties of the multiple regression model.
  • Inference: estimators as random variables, hypothesis testing (assumptions and methods, t-test and F-test), p-values, confidence intervals.
  • Functional Form: dummy variables, conversion of discrete variables into sets of dummy variables, quadratic models, models with interactions, use of dummy variables to explore functional form.
  • Exploiting Time Variation: types of samples (cross-sectional, time series, repeated cross-sections and panel data), first-difference models, individual fixed effects models, time fixed effects models, differences-in-differences models.
  • Instrumental Variables: instrumental variables as a method to alleviate omitted variable bias, reduced form estimates, two-stage least squares estimates.

Learning outcomes

If you complete the course successfully, you should be able to:

  • Understand the possibilities and challenges in using quantitative data to learn about the world.
  • Critically evaluate whether statements and theories are supported by sufficient and/or adequate empirical evidence
  • Use the R econometric package to manipulate data, run regressions and interpret the resulting output
  • Propose and evaluate simple research designs to untangle causal relations between variables
  • Understand how to translate theoretical relations into testable hypotheses.


Unseen written exam (3 hrs).

Essential reading

  • James H. Stock and Mark W. Watson, Introduction to Econometrics, Third Edition, Pearson, 2011.
  • Optional: Jeffrey M. Wooldridge, Introductory Econometrics - A Modern Approach, Sixth Edition, South-Western, 2015.

Course information sheets

Download the course information sheets from the LSE website.