And started reading through the first chapter. Ouch. The math is harder than the concepts – but I’m pushing on. I’m going to make an ass of myself by doing teach-ins to the other Robots (one of whom is a Maths major).
Tomorrow I’m going to be outlining an idea for using “capture/recapture” for measuring repeat visitors to 43 Things to the rest of the Robots. This is a good real world application of sampling and probability – but it raises funny issues like “why not just count the repeat visiors”. Sampling makes the data size much more manageable and seems like it would allow for things like measuring the repeat and defect rate by “vintage” (when the sample was taken).
What are the fundamental concepts one should understand? Here’s a crack at a list I gleamed from this book
- axioms and theorems of probability
- random variables and probability distributions
- mathematical expectations
- special distributions (binomial, poisson, normal)
- sampling theory
- estimation theory
- tests of hypothesis and significance
- curve fitting, regression, and coorelation
- analysis of variance
- non parametric tests
What do you think? Is this a good list to start from? I’m thinking of buying the book and moving through chapter by chapter. I know most of this material at some level, but I’ve been thinking I want to give a short presentation on each section as “proof” I know it. Look out co-workers! You might not have known you’d accomplish this goal as well.