#1306 overall
15.075 Statistics for Applications
MIT OpenCourseWare
1 recommender · 1 thread · recommended in 1 year, 2012 · score 0.7
An MIT OpenCourseWare course on statistics applied to real problems, covering probability, distributions, sampling, estimation and inference, with material on statistical thinking and data analysis. It is a free, self-paced course built from MIT course materials.
The one commenter calls it a good way to tie together distributions, the Central Limit Theorem and estimation after the basics are in place.
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I love stats, but I do not consider myself a professional statistician, so maybe someone else can give you better suggestions.
I'd just say I understand the basic ideas such as DeMoivre or the Central Limit Theorem, and it helps me a lot.
First I'll assume you have a basic understanding of probabilities (odds, dices, cards, etc).
If you don't yet get probabilities, try the CK-12 books probabilities and advanced probabilities book - free on the kindle, and easy to read : http://www.amazon.com/CK-12-Probability-Statistics-Course-eb...
After that, my recommendation is to study the distributions suggested - from Bernoulli to Hypergeometric - and any source you "understand" will do.
The important thing is not the source, but to understand how these things work together, how they "articulate" - i.e. why taking a bunch of samples that follow any distribution will get you something that follow a normal law (LLN, CLT, etc) - even if the law they follow has a big hole in the middle, that'll where the mean of the normal law will be. Or under which conditions you can replace a law by another law, etc.
Then it's a good time to learn what moments do - how they shape the graphs you get. After that, you can try intervals - calculate intervals given a population parameters to see how a sample can predictably differ, then from a sample of a given size how you can estimate the population parameters.
After learning all that, to bind all this knowledge I'd suggest the free courseware on MIT 15_075 (even reading only the slides online on http://ocw.mit.edu/courses/sloan-school-of-management/15-075...)
I've recently "refreshed" my knowledge of statistics, and used the slides from 15 075 as a base. They get to the point and give a better mathematical understanding - something important to build your knowledge on a solid base after you understand how the things work together and what to go down the rabbit hole.
The course suggests the Tamhane and Dunlop book (which I haven't purchased yet but which is on my buy list) ; some other people recommended it to me for the demonstrations - I did the E(S^2n) E(S^n-1) by hand and I would love to see the proof for the Chi2 stuff, because I usually understand better after I see or do the demonstration.
Regarding Chi2, "Introduction to business statistics" has a great chapter #13, giving practical application, but I strongly suggest you understand the basics first - it's too easy to make mistakes with statistics.
Yes I don't fully trust myself with a tool as powerful as statistics - it takes a professional - but even with my limited understanding, I can see the value it provides, the warnings it gives (ie the article read like some basic logical stuff, but then I realized it wouldn't have been that obvious if I hadn't known basic statistics.)