Analytics


Pre requisite:

  • Fund. Maths / numeracy skills bootcamp

Pre-reading:

  • Thomas H. Davenport, “Competing on Analytics”, Harvard Business Review, Jan 1st 2006
  • “The Use and Misuse of Statistics”, Harvard Management Update, No. U0603C

Reports: “Analytics bundle”, Anthony J. Evans [WIP]

Briefs: House of Commons library

Textbook: Curwin, J, and Slater, R., 2007, Quantitative Methods for Business Decisions, Cengage Learning, 6th Edition

In addition:

  • 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
  • Bernstein, P., 1998 Against the Gods Wiley
  • Wheelan, C., 2013, Naked Statistics Norton

Intent: Integrate policy research into course material to demonstrate the practical relevance of the concepts taught.


 

1. Mind & mathematics

Reading: “Think like a statistician – without the math“, Flowing data, March 4th 2010

The myth of “i’m bad at math” The Atlantic

Video: Fermat’s Last Theorem

2. Descriptive statistics

  • “Krupnik Estate Agents: Measures of location and dispersion” (lecture)
  • Back of the envelope calculation (exercise)

Variance: The small schools myth, Alex Tabarrok

How to Read Histograms and Use Them in R

Annualising data

3. Probability theory

  • Freemark Abbey Winery (case)
  • Problem set on probability theory, permutations & combinations due in class
  • Bayes theorem (brief lecture to debrief PS1)
  • Probability distributions (lecture)

Reading: “How to understand risk in 13 clicks,” BBC News, March 11th 2009

Conditional probability

4. Game theory

  • (Lecture)

5. Inferential statistics

“Taxi for Professor Evans” available here

  • Confidence intervals
  • Significance tests
  • Non-parametric

Reading: “Measure for Measure: The strange science of Francis Galton“, by Jim Holt, The New Yorker, Jan 24th 2005

6. Regression

“The suitcase case” available here

  • Correlation
  • Linear regression

Reading: “Regression Step by Step Using Microsoft Excel” (.pdf)

7. Time series

  • (Lecture)
  • Collecting and Presenting Data (see here)

Exponents: here

8. Workshop I

  • Fermat’s Folly (case)
  • Freakonomics replication (video)
  • Sir Roy Meadow discussion (see Mlodinow, 2008, p118, see here)
  • Games:

9. Group presentations

  • (Presentations)

10. Workshop II

  • Problem set on material thus far (based on previous years examination) due in class
  • Review session

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