From 2006 – 2010 I taught a Quantitative Methods course at ESCP Europe. I enjoyed it immensely and got great feedback from students. Since then I’ve continued to gather material but realise that noone gets to see it. I currently teach QM at Cotrugli Business School, but only a few sessions. So I wanted to make the full course publicly available.
The aim of this website is to provide a (mostly) free guide to basic statistics. It should be of use to anyone with an interest in an MBA level education, and I have attempted to supplement my own presentations with links to some exceptional online resources. I say that it is mostly free because some of the cases are not available online and some articles require a subscription. If you have any difficulties accessing them, or would like me to recommend alternatives, let me know. I appreciate that Harvard cases and academic journals are expensive (regardless of whether you’re enrolled on a program or a member of the public). But they are high quality. Indeed if you are looking for a totally free course then this isn’t for you. The skills you develop in an Analytics course are valuable and important. If you are willing to invest your time in building them, you should also be willing to invest some money. But you can trust my recommendations. Everything below is worth it.
Before we begin
I hope you find this online course useful, but I am also a fan of the old fashioned way. These materials are intended to tie into the following textbook:
 Curwin, J, and Slater, R., 2013, Quantitative Methods for Business Decisions, Cengage Learning, 7th Edition (££)
In fact, the materials are intended to complement this textbook. It is a very good one: well written, full of examples, and plenty of opportunities to test yourself. You could do a lot worse than simply order it now and then work your way through it.
I’m also intrigued by “Calculus Made Easy“, by Silvanus Thompson. It’s antiquated in format but highly directed toward simplifying concepts and engaging with the reader. Daniel Kunin has a wonderful website called “Seeing Theory“, which allows users to visualise basic concepts in statistics. I’ve integrated links into the course below. The University of Bristol Medical school has a lovely Research Methods & Statistics online course.
There are also lots of proper online courses to choose from. The only one I have direct experience of is this:
 Quantitative Methods Online Course, Harvard Business School (£££)
If you really need to develop your QM skills then I would recommend you follow the HBS one (instead of mine). However I found it pretty dull and failed to complete it. I’m hoping that by providing a mixture of content you will find mine more enjoyable.
Funnily enough, a few hours after I’d finished this page, a friend of me showed me this website:
 Introductory Statistics, an online course by Andy Field.
It is full of some excellent tutorials that are presented with a unique style. You may prefer his site to mine.
Software: http://datasplash.com/
I also love this course: Calling Bullshit in the Age of Big Data.
Pop analytics
I believe that a good way to prepare for a subject is to read a book that is captivating. Something that stimulates your interest and encourages you to dig deeper. There are lots of bestsellers that have attempted to communicate mathematical ideas to the educated layperson. My favourite 6 are these:
 Mlodinow, L., 2008, The Drunkard’s Walk: How Randomness Rules Our Lives, Pantheon (£)
 Taleb, N.N., 2001, Fooled by Randomness, Random House (£)
 Bodanis, D., 2001, E=MC^2, Pan Books (£)
 Sing, S., 1997, Fermat’s Last Theorem, Fourth Estate (£)
 Bernstein, P., 1998, Against the Gods, Wiley (£)
 Wheelan, C., 2013, Naked Statistics, Norton (actually, I’ve not read this one yet, but I’ve heard it’s good) (£)
Finally, before you start this course you should read the following:
 Davenport, Thomas H., “Competing on Analytics“, Harvard Business Review, Jan 1st 2006
0. Prerequisite
 If you want to start at the very beginning then take my Numeracy Skills Bootcamp, which covers Fundamentals of Mathematics, some Practice Tests, and a discussion of Gender Differences & Mathematics.
1. Introduction
Prior to the lecture below, you may wish to ask the following question: “How expensive is crude oil?”
Download the handouts here.
Why data needs theory:
 “Hollaback and Why Everyone Needs Better Research Methods“, The Message
 “Theory vs. fact” (email me for instructions)
 The same facts are compatible with any number of different theories
Download the handouts here.
A good homework exercise is the following:
Find an example of a news report that sounds like a big number, but once some relevant calculations are made it is in fact unsubstantial.
Some good examples: here.
Bas pie chart: here.
Some other great resources:
 “The Use and Misuse of Statistics“, Harvard Management Update, No. U0603C (£)
 “Three Things Statistics Textbooks Don’t Tell You” Seth Roberts, December 2005
 “Think like a statistician – without the math“, Flowing data, March 4th 2010
 “How to spot spin and inappropriate use of statistics“, House of Commons Library Standard Note, July 29th 2010
 “6 Ways To Tell Lies From Statistics” Betsey Stevenson and Justin Wolfers
 “Francis Galton and regression to the mean“, Stephen Senn, Significance, 2011
 How much is a trillion?
2. Descriptive statistics
Download the handouts here.
Download and complete Krupnik Estate Agents.xlsx
Additional readings:
 Variance: The small schools myth, Alex Tabarrok, Marginal Revolution
 Histograms: How to Read Histograms and Use Them in R
 Anseau, J., Rounding and significant places, House of Commons Library Standard Note, September 20th 2007
3. Probability theory
The lecture below is very heavy. I suggest the following
 Start off with a group activity: Freemark Abbey Winery (£)
 Collect a Problem set on probability theory, Bayes theorem, permutations, and combinations
 Socrative Quiz: Probability Theory
 Socrative Quiz: Probability Theory: Krupnik Investment
 Socrative Quiz: Probability Theory: Bayes Theorem
 Socrative Quiz: Probability Theory: factorials, permutations and combinations
Download the handouts here.
Seeing Theory: Basic Probability and Compound Probability
Additional readings:
 “How to understand risk in 13 clicks,” BBC News, March 11th 2009
 “Momentous modelling” The Economist, February 3rd 2007
 Tierney, John, “Behind Monty Hall’s Doors: Puzzles, Debate and Answer?” New York Times, July 21st 1991
 Conditional probability
 Bennett, Deborah, J., “A Probability Puzzle“, Fractals of Change, October 5th 2007
 Capen, E.C., 1976 “The Difficulty of Assessing Uncertainty” J Pet Technol 28(8):843850 would be a great way to teach uncertainty that builds into confidence intervals.
Download the handouts here.
Quiz on Distributions (original source here).
Seeing Theory: Compound Probability
 Socrative Quiz: Probability Distributions
4. Inferential statistics
Download the handouts here.
Seeing Theory: Statistical Inference
 Taxi for Professor Evans, June 2018
Download the handouts here.
 Socrative Quiz: Inferential Statistics
Additional reading:
 “Measure for Measure: The strange science of Francis Galton“, by Jim Holt, The New Yorker, Jan 24th 2005
 “A Refresher on Statistical Significance” by Amy Gallo, Harvard Business Review, February 16th 2016
 “Statisticians Found One Thing They Can Agree On: It’s Time To Stop Misusing PValues” by Christy Aschwanden, FiveThirtyEight, March 7th 2016
 “False hope” The Economist, February 21st 2015
 “Statistics Glossary: Hypothesis Testing“
5. Nonparametric Statistics
 Socrative Quiz: Chisquare test
6. Correlation
Download the handouts here.
 Test yourself: http://guessthecorrelation.com/ (my high score is 42)
 Socrative Quiz: Correlation
7. Regression analysis
Seeing Theory: Linear Regression
 The Suitcase Case, June 2018
 Socrative Quiz: Regression
Additional readings
 Gallo, Amy, “A refresher on regression analysis” Harvard Business Review
 Small numbers
 “Regression Step by Step Using Microsoft Excel“
 Ramcharan, R., 2006, “Regressions: Why Are Economists Obssessed with Them?” Finance and Development, 43(1)
 “Running the numbers” The Economist, October 14th 2000
 “Signifying nothing?” The Economist, January 29th 2004
8. Time series
Download the handouts here.
 Socrative Quiz: Time Series
Further reading:

 A First Course on Time Series Analysis (An open source introductory textbook on time series analysis, free to download)
 “An Intuitive Guide To Exponential Functions & e“, Better Explained
 “A new fashion in modelling” The Economist, November 24th 2007
 Bolton, P., Index Numbers, House of Commons Library Standard Note, September 20th 2007
 “Demystifying Chain Volume Measures” Australian Bureau of Statistics, March 2003
 “Annualising data“, Dallas Federal Reserve, DataBasics
 “Shifting the base year” MBA Lectures, Jun 19 2010
 Harford, T., “Houses cost more in the summer: Here’s why“. Financial Times, September 6th 2008
9. Data Presentation
10. Review
 “Fermat’s Folly”, October 2018
 Email me for the case and solutions
You can buy a hard copy of the complete slide deck here, or download a free PDF here.
APPENDIX
A. Controversies
I believe that the best way to internalise the key concepts in this course is to conduct a replication exercise. These have become increasingly common as ways to apply the concepts covered, and test a students knowledge retention. To be honest though I am yet to find any really good examples of statistical tests that companies have utilised, and for which the underlying data set is available.
In their textbook, “Modern Principles: Macroeconomics”, Tyler Cowen and Alex Tabarrok present a good exercise to replicate a Solow Model. My (flawed) attempt to combine two of their problem sets is here:
 Solow 1502.pdf (email me for discussion)
Whilst I continue to look for potential replications, one option is to focus on some controversial statistical debates. These are also good ways to go deeper into the theory, and fully appreciate the link between theory and practice.
 Crime and abortion
 “Crossing continents” BBC Radio 4, December 21st 2006
 Freakonomics video
 Donohue, J.J., and Levitt, S., (2001) “The Impact of Legalised Abortion on Crime”. Quarterly Journal of Economics, Vol. CXVI, Issue 2., pp.379420
 “Oopsonomics” The Economist, December 1st 2005
 Foote, C.L., and Goetz, C.F., “Testing Economic Hypotheses with StateLevel Data: A comment on Donohue and Levitt (2001)”. Federal Reserve Bank of Boston working paper. November 2005
 Levitt, S., “Abortion and crime: who should you believe?” Freakonomics Blog, 15th May 2005
 Sudden Infant Death syndrome
 see Mlodinow, 2008, p118119
 How Juries are Fooled by Statistics, Peter Donnelly, Ted Global 2005
 Roy Meadow Wikipedia page
 Breast cancer
 “Dodgy numbers“, The Remittance Man, April 14th 2014
 Sovereign debt crises
 “The 90% Question” The Economist, April 20th 2013
 MMR
B. Game Theory
Appendix: Handouts
After party
You should now be a savvy consumer of statistical analysis and passionate about good data management. I recommend that you treat yourself to the following tome:
 Gleick, J., 2012 [2011], The Information, Fourth Estate (£)
Thank you for visiting.
Last updated: October 2018