MSAN 504  Review of Probability and Statistics
Instructor: Jeff Hamrick
Course Syllabus
Summer 2013
SUMMARY INFORMATION
Instructor: Jeff Hamrick, Ph.D., CFA, FRM
Office: Masonic 211
Office Hours: Half an hour before and after class.
Cell Phone: 617/9434619
Email Address: jhamrick@usfca.edu
Class Location: 101 Howard Street, Room 451
Class Time: 10:00 a.m.  12:00 noon on Thursdays and Fridays
ON COURSE OBJECTIVES. Any student who successfully completes this boot camp should:
 Understand the definitions of probability mass functions, probability density functions, and cumulative distributions functions, as well as moments and cumulants;
 Know the properties of the most famous examples of random variables (Bernoulli, binomial, exponential, Poisson, normal, etc.);
 Master the underpinnings of the two most common parameter estimation techniques, maximum likelihood estimation and the method of moments;
 Understand the difference between a sample and a population;
 Be able to state the Central Limit Theorem, understand its importance, and apply it in a variety of basic situations;
 Be able to implement, by hand and in R, all elementary one and twosample tests of hypotheses and confidence interval constructions (means, proportions, correlation, ratios of variances, etc.);
 Understand the fundamental axioms, rules, and laws of probability theory;
 Simulate (using R) random numbers governed by various probability distributions using the method of inverse transformation and the acceptancerejection technique;
 Be able to use R to generate standard numerical and visual summaries of data, including the fivenumber summary, boxandwhiskers plots, histograms, kernel density histograms, scatter plots, and stemandleaf plots;
 Define, and work with examples related to, conditional probability;
 Understand the importance of the concept of independence;
 Prove and use the Law of Total Probability;
 Use the Law of Total Probability to prove Bayes' Theorem and be able deploy Bayes' Theorem in a variety of practical situations;
 Understand the difference between, for example, a Bayesian estimator and a traditional point estimator;
 Work with random vectors as well as random variables;
 Be able to state, and explain, the assumptions that drive the standard univariate linear regression model; and
 Be able to generate point estimates for the coefficients in the standard univariate linear regression model by solving the normal equation.

