# Math 483 (Mathematical Statistics)

 Week M W F 1 1.1 - 1.6 Probability review   Hw1 Probability review: conditional Probability review   Hw2 2 2: Random Variables RV continued: transformations, CDF, joint distributions RV continued 3 Labor Day 3: Expected values Expected values   Hw3 4 Expected values: MGFs Chebyshev inequality and Ch.5 5: Convergence of RVs   Hw4 5 Ch.5: Theorem 5.5; Proof of CLT Ch.5: S^2, proof of LLN Ch.5: Delta method   Hw5 6 Estimates, T-distribution handout Properties of estimates: Ch.6 Ch.7: empirical CDF 7 Ch 7: plug-in estimates Review   key Midterm Exam   key 8 Ch 8: bootstrap (See R demo 8 under R stuff) Ch.9: MOM   Hw6 49ers holiday 9 Ch 9: Max. Likelihood Ch.9: MLE properties Ch.9: Fisher information   Hw7 10 Ch 9: F.I. examples (see R code for Cauchy) Ch.9: Multivariate Normal and multipar. Fisher information Ch.9: multiparameter Fisher information, Delta-method   Hw8 11 Ch.9: Multiparameter Delta-method Ch.9: Gamma example (see R code ) Ch.10: Hypothesis testing 12 Ch.10: Wald test   Hw9 Ch.10: Chi-square test (see Math382 textbook, Chapter 10.) Ch.10: LR test handout 13 Ch.10: LR test: multiparameter Review for Exam 2 practice   key Exam 2   key 14 Ch.12: Bayesian inference Ch.12: Bayesian inference   Hw10 Thanksgiving break 15 Ch.12: Bayesian inference Ch.12: Bayesian inference Ch.24: Gibbs sampler   Hw11 16 Ch.24: Gibbs sampler cont'd   (see R code ) Ch.13: Linear regression Ch.13: Linear regression   Final Practice   key

Final week office hours: M,W 1-3pm
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