Courses

AMS 507 Introduction to Probability 
The topics include sample spaces, axioms of probability, conditional probability and independence, discrete and continuous random variables, jointly distributed random variables, characteristics of random variables, law of large numbers and central limit theorem, Markov chains. Note: Crosslisted with HPH 696.
Fall, 3 credits, ABCF grading 
AMS 507 webpage

AMS 569 Probability Theory I 
Probability spaces and sigma-algebras. Random variables as measurable mappings. Borel-Cantelli lemmas. Expectation using simple functions. Monotone and dominated convergence theorems. Inequalities. Stochastic convergence. Characteristic functions. Laws of large numbers and the central limit theorem. This course is offered as both AMS 569 and MBA 569.
Prerequisite: AMS 504 or equivalent 
AMS 569 webpage 
3 credits, ABCF grading

AMS 570 Introduction to Mathematical Statistics 
Probability and distributions; multivariate distributions; distributions of functions of random variables; sampling distributions; limiting distributions; point estimation; confidence intervals; sufficient statistics; Bayesian estimation; maximum likelihood estimation; statistical tests.
Prerequisite: AMS 507
Spring, 3 credits, ABCF grading 
AMS 570 webpage 

AMS 571 Mathematical Statistics 
Sampling distribution; convergence concepts; classes of statistical models; sufficient statistics; likelihood principle; point estimation; Bayes estimators; consistency; Neyman-Pearson Lemma; UMP tests; UMPU tests; Likelihood ratio tests; large sample theory. 
Prerequisite: AMS 570 is preferred but not required 
Fall, 3 credits, ABCF grading
AMS 571 webpage 

AMS 572 Data Analysis I 
Introduction to basic statistical procedures. Survey of elementary statistical procedures such as the t-test and chi-square test. Procedures to verify that assumptions are satisfied. Extensions of simple procedures to more complex situations and introduction to one-way analysis of variance. Basic exploratory data analysis procedures (stem and leaf plots, straightening regression lines, and techniques to establish equal variance). Coscheduled as AMS 572 or HPH 698. 
Fall, 3 credits, ABCF grading
AMS 572 webpage 

AMS 573 Design and Analysis of Categorical Data 
Measuring the strength of association between pairs of categorical variables. Methods for evaluating classification procedures and inter-rater agreement. Analysis of the associations among three or more categorical variables using log linear models. Logistic regression. 
Spring, 3 credits, ABCF grading
AMS 573 webpage 

AMS 575 Internship in Statistical Consulting 
Directed quantitative research problem in conjunction with currently existing research programs outside the department. Students specializing in a particular area work on a problem from that area; others work on problems related to their interests, if possible. Efficient and effective use of computers. Each student gives at least one informal lecture to his or her colleagues on a research problem and its statistical aspects. 
Prerequisite: Permission of instructor 
Fall and Spring, 3-4 credits, ABCF grading 
AMS 575 webpage 

AMS 577 Multivariate Analysis 
The multivariate distribution. Estimation of the mean vector and covariance matrix of the multivariate normal. Discriminant analysis. Canonical correlation. Principal components. Factor analysis. Cluster analysis. 
Prerequisites: AMS 572 and AMS 578 
3 credits, ABCF grading
AMS 577 webpage 

AMS 580 Statistical Learning
This course teaches the following fundamental topics: (1) General and Generalized Linear Models; (2) Basics of Multivariate Statistical Analysis including dimension reduction methods, and multivariate regression analysis; (3) Supervised and unsupervised statistical learning.
Spring, 3 credits, ABCF grading
AMS 580 Webpage

AMS 582 Design of Experiments 
Discussion of the accuracy of experiments, partitioning sums of squares, randomized designs, factorial experiments, Latin squares, confounding and fractional replication, response surface experiments, and incomplete block designs. Coscheduled as AMS 582 or HPH 699. Prerequisite: AMS 572 or equivalent 
Fall, 3 credits, ABCF grading 
AMS 582 webpage 

AMS 585 Internship in Data Science 
Directed data science problem in conjunction with currently existing research programs outside the department. Students specializing in a particular area work on a problem from that area; others work on problems related to their interests, if possible. Efficient and effective use of computers. Each student gives at least one informal lecture to his or her colleagues on a research problem and its statistical aspects.
3 credits, ABCF grading
AMS 585 Webpage

AMS 586 Time Series 
Analysis in the frequency domain. Periodograms, approximate tests, relation to regression theory. Pre-whitening and digital fibers. Common data windows. Fast Fourier transforms. Complex demodulation, GibbsÕ phenomenon issues. Time-domain analysis.
Prerequisites: AMS 507 and AMS 570 
Fall, 3 credits, ABCF grading
AMS 586 webpage 

AMS 587 Nonparametric Statistics 
This course covers the applied nonparametric statistical procedures: one-sample Wilcoxon tests, two-sample Wilcoxon tests, runs test, Kruskal-Wallis test, KendallÕs tau, SpearmanÕs rho, Hodges-Lehman estimation, Friedman analysis of variance on ranks. The course gives the theoretical underpinnings to these procedures, showing how existing techniques may be extended and new techniques developed. An excursion into the new problems of multivariate nonparametric inference is made.
3 credits, ABCF grading
AMS 587 webpage 

AMS 588 Failure and Survival Data Analysis
Statistical techniques for planning and analyzing medical studies. Planning and conducting clinical trials and retrospective and prospective epidemiological studies. Analysis of survival times including singly censored and doubly censored data. Quantitative and quantal bioassays, two-stage assays, routine bioassays. Quality control for medical studies. 
3 credits, ABCF grading
AMS 588 Webpage 

AMS 598 Big Data Analysis
Introduction to the application of the supercomputing for statistical data analyses, particularly on big data.
Prerequisites:  AMS 572, AMS 573 and AMS 578
Fall, 3 credits, ABCF grading
AMS 598 Webpage