For T 8/24, read J. Patrick Harrington's (U MD) basic statistics primer/review.  This will either remind you or introduce you to the basic key distributions in astronomy, and some of the salient results.  Note in particular the use of moment equations to define the mean and variance.  As we've said, these are used in other areas of physics (esp. statistical physics), and we may someday see more when we take moments of the radiative transfer equation in a future stellar atmospheres course.  I also want you to be sure that you don't drift off when reading the section on least squares fitting.  First, note the math and derivations look downright ugly with a quick glance, but I think even with a tiny bit of attention they are really easy to follow.  Second, note that this is a generalized technique-- you could apply a "least squares" approach to fitting any function.  Third, note that this is a so-called "maximum likelihood estimate" of a best-fit line.  Maximum likelihood techniques generally maximize likelihood by minimizing the variance (the sum of the squares of the residuals between the data y values and the fitted value at some x) with respect to relevant parameters of the fitting function (for a line, the slope and zero-point). 
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For the first class Th 8/19  Read sections 1 and 2 of  this introductory review of Bayesian inference by Tom Loredo.   Your focus should be on a (perhaps initially nebulous) qualitative understanding of Bayes theorem and how Bayesian inference differs from the "frequentist" approach.  You are encouraged to look at some of the sample applications in later sections of the article (they might be useful for homework)-- particularly the subtle art of establishing prior probabilities-- but we will try to establish "ok, so how do I actually use this?" in class. 
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