  01  MWF  9:00A10:00A  Crow / 206  Cox  May 4 2017 3:30PM  5:30PM  75  54  0   

  01  MWF  10:00A11:00A  Wilson / 214  Freiwald  May 5 2017 10:30AM  12:30PM  125  107  0   
 02  MWF  11:00A12:00P  Wilson / 214  Freiwald  May 5 2017 10:30AM  12:30PM  120  95  0   
 F  T  11:00A12:00P  Duncker / 3  Mancuso  See Department  32  21  0   
 H  T  12:00P1:00P  Duncker / 3  Mancuso  See Department  32  31  0   

  01  MWF  9:00A10:00A  Hillman / 60  Stern  May 5 2017 10:30AM  12:30PM  125  72  0   
 02  MWF  10:00A11:00A  Hillman / 60  Stern  May 5 2017 10:30AM  12:30PM  140  134  0   

 Description:  An elementary introduction to probability and statistics. Discrete and continuous random variables, mean and variance, hypothesis testing and confidence limits, nonparametric methods, Student's t, analysis of variance (ANOVA), (multiple) regression, contingency tables. Graphing calculator with statistical distribution functions (such as the TI83) is required. Students considering a major or minor in mathematics should take Math 3200, NOT Math 2200. Examination Schedule: Tests, at which attendance is required, will be given from 6:30 to 8:30 p.m. on the following dates: Tuesday February 7, Tuesday March 7, and Tuesday April 4. Prerequisite: Math 131. 

  01  MWF  11:00A12:00P  Busch / 100  Blank  May 4 2017 3:30PM  5:30PM  100  70  0   
 02  MWF  12:00P1:00P  Busch / 100  Blank  May 4 2017 3:30PM  5:30PM  100  91  0   

  01  MWF  11:00A12:00P  Rebstock / 215  Krantz  May 4 2017 3:30PM  5:30PM  169  140  0   

  01  MWF  12:00P1:00P  Seigle / 208  Chi  May 10 2017 10:30AM  12:30PM  45  23  0   

 Description:  An introductory course in linear algebra that focuses on Euclidean nspace, matrices and related computations. Topics include: systems of linear equations, row reduction, matrix operations, determinants, linear independence, dimension, rank, change of basis, diagonalization, eigenvalues, eigenvectors, orthogonality, symmetric matrices, least square approximation, quadratic forms. Introduction to abstract vector spaces. Tests, at which attendance is required, will be given from 6:308:30 p.m. on Wednesday February 22, and Monday April 3.
Prerequisite: Math 132. 

  01  MWF  10:00A11:00A  Brown / 100  Shapiro  May 8 2017 10:30AM  12:30PM  230  206  0   

  01  MWF  1:00P2:00P  Busch / 100  Shareshian  May 10 2017 1:00PM  3:00PM  101  71  0   

  01  MWF  2:00P3:00P  Crow / 204  Huo  May 8 2017 3:30PM  5:30PM  90  73  0   

 Description:  An introduction to probability and statistics. Discrete and continuous random variables, mean and variance, hypothesis testing and confidence limits, Bayesian inference, nonparametric methods, Student's ttest, contingency table analysis, multifactor analysis of variance, random effects models, mixed models, multiple regression, maximum likelihood and logistic regression. Graphing calculator with Z, t, chisquare and F distribution functions (such as the TI83 series) may be required. Calculus and the SAS software package are both used in an essential way. EXAMINATION SCHEDULE: Tests, at which attendance is required, will be given from 6:308:30 p.m. on Tuesday February 7, Tuesday March 7, and Tuesday April 4. Prerequisite: Math 233 or permission of the instructor. 

  01  MWF  1:00P2:00P  Hillman / 60  Ding  May 4 2017 3:30PM  5:30PM  105  94  0   
 02  MWF  2:00P3:00P  Hillman / 60  Ding  May 4 2017 3:30PM  5:30PM  85  55  0   

  01  MWF  1:00P2:00P  Duncker / 101  Wickerhauser  May 10 2017 1:00PM  3:00PM  55  49  0   

  01  TBA   TBA  Beheshti  See Department  999  0  0   
 02  TBA   TBA  Blank  See Department  999  0  0   
 03  TBA   TBA  Chi  See Department  999  0  0   
 04  TBA   TBA  Ding  See Department  999  0  0   
 05  TBA   TBA  Feres  See Department  999  0  0   
 06  TBA   TBA  Freiwald  See Department  0  0  0   
 07  TBA   TBA  Goldring  See Instructor  999  0  0   
 08  TBA   TBA  Kerr  See Department  999  0  0   
 09  TBA   TBA  Knese  See Department  999  0  0   
 10  TBA   TBA  Krantz  See Department  999  0  0   
 11  TBA   TBA  Kuffner  See Department  999  0  0   
 12  TBA   TBA  Kumar  See Department  999  0  0   
 13  TBA   TBA  Lin  See Department  999  0  0   
 14  TBA   TBA  McCarthy  See Department  999  0  0   
 15  TBA   TBA  Roberts  See Department  999  0  0   
 16  TBA   TBA  Shapiro  See Department  999  0  0   
 17  TBA   TBA  Shareshian  See Department  999  1  0   
 18  TBA   TBA  Spitznagel  See Department  999  0  0   
 19  TBA   TBA  Stern  See Department  999  0  0   
 20  TBA   TBA  Tang  See Department  999  2  0   
 21  TBA   TBA  Wick  See Department  999  0  0   
 22  TBA   TBA  Wickerhauser  See Department  999  0  0   
 23  TBA   TBA  Wright  See Department  999  1  0   
 24  TBA   TBA  FigueroaLopez  See Department  999  1  0   

  01  MWF  3:00P4:00P  Seigle / 208  Chi  May 4 2017 6:00PM  8:00PM  30  15  0   

  01  MWF  11:00A12:00P  Duncker / 101  Feres  May 9 2017 10:30AM  12:30PM  50  43  0   

  01  MWF  3:00P4:00P  Eads / 103  Wickerhauser  May 4 2017 6:00PM  8:00PM  40  37  0   

  01  TR  11:30A1:00P  Eads / 203  Lin  May 8 2017 1:00PM  3:00PM  40  34  0   

 Description:  Theory of estimation, minimum variance and unbiased estimators, maximum likelihood theory, Bayesian estimation, prior and posterior distributions, confidence intervals for general estimators, standard estimators and distributions such as the Studentt and Fdistribution from a more advanced viewpoint, hypothesis testing, the NeymannPearson Lemma (about best possible tests), linear models, and other topics as time permits. Prereq: Math 3200 and 493, or permission of the instructor. 

  01  MWF  2:00P3:00P  Wilson / 214  Lin  May 8 2017 3:30PM  5:30PM  115  99  0   

  01  MW  4:00P5:30P  Duncker / 101  Feres  May 5 2017 6:00PM  8:00PM  60  52  0   

  20  TBA   TBA  Tang  See Department  999  0  0   
 21  TBA   TBA  Wick  Default  none  999  0  0   
 22  TBA   TBA  Wickerhauser  Default  none  999  0  0   
 23  TBA   TBA  Wright  Default  none  999  0  0   
 24  TBA   TBA  FigueroaLopez  Default  none  999  0  0   

  01  TBA   TBA  Beheshti Zavareh  See Instructor  999  0  0   
 02  TBA   See Dept /  Blank  See Department  5  0  0   
 03  TBA   See Dept /  Chi  See Department  5  0  0   
 04  TBA   See Dept /  Ding  See Department  999  1  0   
 05  TBA   See Dept /  Feres  See Department  999  1  0   
 06  TBA   See Dept /  Freiwald  See Department  5  0  0   
 08  TBA   See Dept /  Kerr  See Department  5  2  0   
 09  TBA   See Dept /  Knese  See Department  7  0  0   
 10  TBA   See Dept /  Krantz  See Department  5  1  0   
 11  TBA   TBA  Kuffner  See Department  999  1  0   
 12  TBA   See Dept /  Kumar  See Department  5  1  0   
 13  TBA   See Dept /  Lin  See Department  5  1  0   
 14  TBA   TBA  McCarthy  See Instructor  0  0  0   
 15  TBA   See Dept /  Roberts  See Department  5  0  0   
 16  TBA   TBA  Shapiro  See Instructor  999  0  0   
 17  TBA   TBA  Shareshian  See Instructor  999  2  0   
 18  TBA   See Dept /  Spitznagel  See Department  7  0  0   
 19  TBA   See Dept /  Stern  See Department  5  0  0   
 20  TBA   See Dept /  Tang  See Department  7  0  0   
 21  TBA   See Dept /  Wick  See Department  5  1  0   
 22  TBA   See Dept /  Wickerhauser  See Department  5  0  0   
 23  TBA   See Dept /  Wright  See Department  5  0  0   
 24  TBA   See Dept /  FigueroaLopez  See Department  5  1  0   

  01  MWF  2:00P3:00P  Crow / 205  Lincoln  May 5 2017 1:00PM  3:00PM  30  6  0   

  01  TR  1:00P2:30P  Rudolph / 102  Beheshti Zavareh  See Department  20  18  0   

 Description:  This course is an introduction to basic statistical analysis for graduate students in medicine, biology, and public health. Students will be introduced to core statistical tools used to study human health outcomes. Topics include: measurement, descriptive analysis, correlation, graphical analysis, hypothesis testing, confidence intervals, analysis of variance, and regression analysis. Major components of the course include learning how to collect, manage, and analyze data using computer software, and how to effectively communicate to others results from statistical analyses. The second aspect of the course is focused on the statistical package R, which is is the most powerful, extensively featured, and capable statistical computing tool available. Course may not be used for credit in undergraduate math major/minor programs, nor in any Mathematics or Statistics graduate programs. Prerequisite: Current graduate enrollment in a program in DBBS, medicine or public health, or permission of instructor.


  01  TBA   TBA  Beheshti Zavareh  See Department  999  0  0   
 02  TBA   See Dept /  Blank  See Department  5  0  0   
 03  TBA   See Dept /  Chi  See Department  5  0  0   
 04  TBA   See Dept /  Ding  See Department  999  2  0   
 05  TBA   See Dept /  Feres  Default  none  999  0  0   
 06  TBA   See Dept /  Freiwald  See Department  5  0  0   
 07  TBA   See Dept /  Goldring  See Department  0  0  0   
 08  TBA   See Dept /  Kerr  See Department  5  1  0   
 09  TBA   See Dept /  Knese  See Department  7  0  0   
 10  TBA   See Dept /  Krantz  See Department  5  1  0   
 11  TBA   TBA  Kuffner  See Department  999  3  0   
 12  TBA   See Dept /  Kumar  See Department  5  0  0   
 13  TBA   See Dept /  Lin  See Department  5  1  0   
 14  TBA   TBA  McCarthy  See Department  999  2  0   
 15  TBA   See Dept /  Roberts  See Department  5  0  0   
 16  TBA   TBA  Shapiro  See Department  999  0  0   
 17  TBA   TBA  Shareshian  See Department  999  1  0   
 18  TBA   See Dept /  Spitznagel  See Department  5  1  0   
 19  TBA   See Dept /  Stern  See Department  5  0  0   
 20  TBA   See Dept /  Tang  See Department  7  1  0   
 21  TBA   See Dept /  Wick  See Department  5  2  0   
 22  TBA   See Dept /  Wickerhauser  See Department  5  0  0   
 23  TBA   See Dept /  Wright  See Department  5  0  0   
 24  TBA   See Dept /  FigueroaLopez  See Department  5  2  0   

