Statistics is probably the most useful course I did as part of my PhD coursework. Statistics is widely applicable to understanding real world information, testing scientific hypothesis, forming models from observed data and so on. Being intrinsic part of the scientific method, it is used in economics, computational search, random algorithms and so on.
In this course, we learnt the techniques of fitting parameters into observed data, and testing hypotheses (method of moments, maximum likelihood estimate and bootstrap methods). Distributions related to normal distribution play an important role in estimating parameters and their variance from actual value.
Our textbook: Mathematical Statistics and Data Analysis (Statistics) by John A. Rice. The book is very well-written and understandable. Technical aspects are "smoothed out" and made accessible and useful to more general audience. Cramster has several solutions to problems from the book. Here is the errata list from the book.
Other Resources
Another course on statistics is here. Read the comments on hypothesis testing here. Lectures on Decision Theory and Bayesian Inference.
SPSS and SAS are widely used statistical packages. Another free statistical tool is the GNU-S, also know as R. An R-tutorial is here.
Saturday, October 25, 2008
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