Courses - math.umn.edu

Institute of Technology One Stop Directories Search U of M

	School of Mathematics Financial Mathematics
Courses

Master of Financial Mathematics with Emphasis in Statistics

For an Emphasis in Statistics, please take

STAT 5302 and
any two of STAT 5401, STAT 5601, STAT 8511.

These courses are offered by the Statistics Department, and are recommended to those students in our program who wish to enhance their knowledge of statistics.

These courses may be taken at any time.

WARNING: Courses listed on this website may have prerequisites; see the syllabi to check on those. If you have any question about whether you are ready to take a certain course, please speak to the course instructor and/or your advisor.

NOTE: Completion of these "with Emphasis in Statistics" requirements also fulfills the requirements for a Master's level minor in Statistics. If you wish to obtain that minor, you'll need to get the signature of the Statistics DGS on your degree program form.

STAT 5302 Applied Regression Analysis 4 credits Syllabus: Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications.
STAT 5401 Applied Multivariate Methods 3 credits Syllabus: Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve and profile analysis. Canonical correlation analysis. Principle components and factor analysis. Discrimination, classification, and clustering.
STAT 5601 Nonparametric Methods 3 credits Syllabus: Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models.
STAT 8511 Time Series Analysis 3 credits Syllabus: Time series as multivariate samples of size 1. Discrete and continuous parameter time series. Stationarity. Autocovariance and autocorrelation. ARIMA models, identification, estimation, diagnostic checking. Other time domain models. Forecasting, seasonal adjustment, time series regressions. Introduction to frequency domain methods.

Financial Mathematics
(612) 625-1306 mfmath@umn.edu
127 Vincent Hall
206 Church St. S.E.
Minneapolis, MN 55455 USA

www.math.umn.edu/finmath/courses/mfm_emphasis_in_statistics/index.shtml
The University of Minnesota is an equal opportunity educator and employer.
© 2009, The Regents of the University of Minnesota



		Enter keyword search
		Search Domain