Instructor(s):  Dr. John G Del Greco

Required text(s):  E. B. Saff and Snider, A. D., Fundamentals of Complex Analysis with Applications to Engineering and Science. 3rd ed. New Jersey: Pearson, 2003. Print.

Prerequisites:  MATH 263

Course description:  In complex analysis we are interested in extending results using real numbers in algebra and analysis to analogous results using the field of complex numbers. Applications of this extension occur in electrical engineering, signal processing, quantum mechanics, and various mathematical fields such as number theory and real analysis. Many concepts and results that seem non-intuitive when encountered in real analysis become “natural” when extended to their complex versions. We will study analytic functions, integration, infinite series, residue theory and conformal mappings.

Instructor(s):  Dr. John G Del Greco

Required text(s):  E. B. Saff and Snider, A. D., Fundamentals of Complex Analysis with Applications to Engineering and Science. 3rd ed. New Jersey: Pearson, 2003. Print.

Prerequisites:  MATH 263

Course description:  In complex analysis we are interested in extending results using real numbers in algebra and analysis to analogous results using the field of complex numbers. Applications of this extension occur in electrical engineering, signal processing, quantum mechanics, and various mathematical fields such as number theory and real analysis. Many concepts and results that seem non-intuitive when encountered in real analysis become “natural” when extended to their complex versions. We will study analytic functions, integration, infinite series, residue theory and conformal mappings.

Instructor(s):  Timothy E. O'Brien, PhD

Required text(s):  http://users.stat.umn.edu/~gary/Book.html We'll use Gary Oehlert's book (link above) and outside materials (provided as handouts or via Sakai).

Prerequisites:  For STAT-307: (STAT 203 or STAT 335 with a C- or better) and STAT 308 with a C- or better For STAT-407: Grad student standing, but STAT-408 or equivalent is strongly recommended.

Course description:  See the Loyola official description, which supersedes the following: In order to understand how scientific or industrial processes or systems work, researchers in engineering, biology, chemistry, medicine, agronomy, etc. set up randomized studies to answer fundamental questions. The field of statistical experimental design is a branch of statistics that is devoted to finding efficient and practical ways to run these studies. Then, once the study is run and the data are obtained, the statistician is called upon to analyze the data, interpret the results, reach the correct conclusions, and to communicate the conclusions to the original researchers and decision-makers. This course provides students with a thorough introduction to statistical experimental design and to the statistical methods used to analyze the resulting data. The concepts of comparative experiments, ANOVA and mean separation procedures will be reviewed; blocking (complete and incomplete) will be discussed, as will be factorial designs, fractional factorial designs, confounding, mixed models, response surfaces and mixture experiments. This course focuses on applications such as from agricultural, engineering, medicine and environmental research, and other examples will also be provided to show the breadth of applications.

Instructor(s):  Timothy E. O'Brien, PhD

Required text(s):  http://users.stat.umn.edu/~gary/Book.html We'll use Gary Oehlert's book (link above) and outside materials (provided as handouts or via Sakai).

Prerequisites:  For STAT-307: (STAT 203 or STAT 335 with a C- or better) and STAT 308 with a C- or better For STAT-407: Grad student standing, but STAT-408 or equivalent is strongly recommended.

Course description:  See the Loyola official description, which supersedes the following: In order to understand how scientific or industrial processes or systems work, researchers in engineering, biology, chemistry, medicine, agronomy, etc. set up randomized studies to answer fundamental questions. The field of statistical experimental design is a branch of statistics that is devoted to finding efficient and practical ways to run these studies. Then, once the study is run and the data are obtained, the statistician is called upon to analyze the data, interpret the results, reach the correct conclusions, and to communicate the conclusions to the original researchers and decision-makers. This course provides students with a thorough introduction to statistical experimental design and to the statistical methods used to analyze the resulting data. The concepts of comparative experiments, ANOVA and mean separation procedures will be reviewed; blocking (complete and incomplete) will be discussed, as will be factorial designs, fractional factorial designs, confounding, mixed models, response surfaces and mixture experiments. This course focuses on applications such as from agricultural, engineering, medicine and environmental research, and other examples will also be provided to show the breadth of applications.