  01  TR  8:30A10:00A  Crow / 205  Stockdale  Dec 14 2018 1:00PM  3:00PM  30  17  0   

  01  MWF  9:00A10:00A  Brown / 100  Johnson  Dec 13 2018 3:30PM  5:30PM  175  158  0   
 02  MWF  10:00A11:00A  Brown / 100  Johnson  Dec 13 2018 3:30PM  5:30PM  175  172  0   

  01  MWF  9:00A10:00A  Brown / 118  Thornton  Dec 14 2018 10:30AM  12:30PM  170  139  0   
 02  MWF  10:00A11:00A  Brown / 118  Thornton  Dec 14 2018 10:30AM  12:30PM  175  174  0   
 03  MWF  12:00P1:00P  Louderman / 458  Shrensker  Dec 14 2018 10:30AM  12:30PM  150  141  0   
 04  MWF  1:00P2:00P  Louderman / 458  Shrensker  Dec 14 2018 10:30AM  12:30PM  150  124  0   
 C  T  9:00A10:00A  Eads / 215  Covey  Default  none  30  30  0   
 I  T  10:00A11:00A  Eads / 215  Barker  Default  none  30  30  0   
 N  T  10:00A11:00A  Simon / 018  Rodriguez  Default  none  30  29  0   
 O  T  11:00A12:00P  Eads / 215  Covey  Default  none  30  21  0   
 T  T  11:00A12:00P  Simon / 018  Rodriguez  Default  none  30  13  0   
 Y  T  12:00P1:00P  Simon / 018  Rodriguez  Default  none  30  24  0   

 Description:  This is a one credit course, that can only be taken concurrently with Math 131, Calculus
I. The purpose of the course is to show how mathematics can solve real world problems, and how calculus dramatically expands the range of problems that can be tackled. Each class will be devoted to the analysis of some problems, which may include: dimensional analysis, the mathematics of convoys, Fibonacci numbers, fractals, linear regression, Euclid's algorithm, Stein's algorithm, network capacities, Braess's paradox, Galton's approach to surnames, how genes spread through populations, SIR model of infectious diseases.
The first few classes will not use differentiation.
Must be taken concurrently with Math 131.
Refere 

 Description:  Introduction to ordinary differential equations: firstorder equations, linear equations, systems of equations, series solutions, Laplace transform methods, numerical solutions. Prerequisite: successful completion of, or concurrent enrollment in, Math 233. EXAMINATION SCHEDULE: Tests, at which attendance is required, will be given from 6:30 to 8:30 p.m. on September 17, October 9, and November 13. 

  01  MWF  9:00A10:00A  Wilson / 214  Bongers  Dec 14 2018 10:30AM  12:30PM  100  60  0   
 02  MWF  11:00A12:00P  Wilson / 214  Bongers  Dec 14 2018 10:30AM  12:30PM  120  120  1   

 Description:  An elementary introduction to statistical concepts, reasoning and data analysis. Topics include statistical summaries and graphical presentations of data, discrete and continuous random variables, the logic of statistical inference, design of research studies, point and interval estimation, hypothesis testing, and linear regression. Students will learn a critical approach to reading statistical analyses reported in the media, and how to correctly interpret the outputs of common statistical routines for fitting models to data and testing hypotheses. A major objective of the course is to gain familiarity with basic R commands to implement common data analysis procedures. Students intending to pursue a major or minor in mathematics or wishing to take 400 level or above statistics courses should instead take Math 3200.
EXAMINATION SCHEDULE: Tests, at which attendance is required, will be given from 6:30 to 8:30 p.m. on September 20, October 10, and November 14.
Prereqs: Math 131 

  01  MWF  9:00A10:00A  Hillman / 70  Wickerhauser  Dec 13 2018 3:30PM  5:30PM  100  52  0   
 02  MWF  11:00A12:00P  Brown / 118  Lin  Dec 13 2018 3:30PM  5:30PM  120  102  0   

 Description:  Differential and integral calculus of functions of two and three variables. Vectors, curves and surfaces in space, partial derivatives, multiple integrals, line integrals, vector calculus through Green's Theorem. Prerequisite: Math 132, or a score of 4 or 5 on the Advanced Placement Calculus Examination (BC version). EXAMINATION SCHEDULE: Tests, at which attendance is required, will be given from 6:30 to 8:30 p.m. on September 20, October 10, and November 14. 

  01  MWF  9:00A10:00A  Hillman / 60  Gallardo Candela  Dec 13 2018 3:30PM  5:30PM  150  72  0   
 02  MWF  10:00A11:00A  Louderman / 458  Vittert  Dec 13 2018 3:30PM  5:30PM  150  94  0   
 03  MWF  12:00P1:00P  Rebstock / 215  Shareshian  Dec 13 2018 3:30PM  5:30PM  185  178  0   
 04  MWF  1:00P2:00P  Rebstock / 215  Shareshian  Dec 13 2018 3:30PM  5:30PM  195  192  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. Prerequisite: Math 132. EXAMINATION SCHEDULE: Insemester exams, at which attendance is required, will be given from 6:30 to 8:30 p.m. on October 8, and November 12. 

  01  MWF  9:00A10:00A  McDonnell / 162  Kerr  Dec 17 2018 10:30AM  12:30PM  100  59  0   
 02  MWF  10:00A11:00A  McDonnell / 162  Kerr  Dec 17 2018 10:30AM  12:30PM  120  111  0   

  01  MWF  1:00P2:00P  Busch / 100  Krantz  Dec 19 2018 1:00PM  3:00PM  100  93  0   

  01  MWF  2:00P3:00P  Seigle / 109  Chi  Dec 17 2018 3:30PM  5:30PM  25  21  0   

  01  MWF  12:00P1:00P  Wilson / 214  Escobar Vega  Dec 19 2018 10:30AM  12:30PM  70  61  0   

 Description:  An introduction to probability and statistics. Major topics include elementary probability, special distributions, experimental design, exploratory data analysis, estimation of mean and proportion, hypothesis testing and confidence, regression, and analysis of variance. Emphasis is placed on development of statistical reasoning, basic analytic skills, and critical thinking in empirical research studies. The use of the statistical software R is integrated into lectures and weekly assignments. Required for students pursuing a major or minor in mathematics or wishing to take 400 level or above statistics courses.
EXAMINATION SCHEDULE: Tests, at which attendance is required, will be given from 6:30 to 8:30 p.m. on September 17, October 9, and November 13.
Prereqs: Math 132. Though Math 233 is not essential, it is recommended. 

  01  MWF  9:00A10:00A  Rebstock / 215  Syring  Dec 14 2018 10:30AM  12:30PM  125  112  0   

  01  TBA   TBA  Beheshti Zavareh  See instructor  999  0  0   
 02  TBA   TBA  Blank  See instructor  999  0  0   
 03  TBA   (None) /  [TBA]  See instructor  999  0  0   
 04  TBA   TBA  Chi  See instructor  999  0  0   
 05  TBA   TBA  Ding  See instructor  999  0  0   
 06  TBA   (None) /  [TBA]  See instructor  999  0  0   
 07  TBA   TBA  Feres  See instructor  999  0  0   
 08  TBA   TBA  FigueroaLopez  See instructor  999  0  0   
 09  TBA   TBA  Frankel  See instructor  999  0  0   
 10  TBA   TBA  Kerr  See instructor  999  0  0   
 11  TBA   TBA  Knese  See instructor  999  0  0   
 12  TBA   TBA  Krantz  See instructor  999  0  0   
 13  TBA   TBA  Kuffner  See instructor  999  0  0   
 14  TBA   TBA  Kumar  See instructor  999  0  0   
 15  TBA   TBA  Lin  See instructor  999  0  0   
 16  TBA   TBA  McCarthy  See instructor  999  0  0   
 17  TBA   (None) /  [TBA]  See instructor  999  0  0   
 18  TBA   TBA  Roberts  See instructor  999  0  0   
 19  TBA   TBA  Shapiro  See instructor  999  0  0   
 20  TBA   TBA  Shareshian  See instructor  999  0  0   
 21  TBA   TBA  Song  See instructor  999  0  0   
 22  TBA   TBA  Spitznagel  See instructor  999  0  0   
 23  TBA   TBA  Stern  See instructor  999  0  0   
 24  TBA   TBA  Tang  See instructor  999  0  0   
 25  TBA   TBA  Wickerhauser  See instructor  999  0  0   
 26  TBA   TBA  Wick  See instructor  999  0  0   
 27  TBA   TBA  Wright  See instructor  999  0  0   

  01  MWF  10:00A11:00A  Duncker / 101  Precup  Dec 17 2018 10:30AM  12:30PM  60  60  0   

  01  MWF  12:00P1:00P  Seigle / 301  Chi  Dec 19 2018 10:30AM  12:30PM  50  46  0   

 Description:  Theory and practice of linear regression, analysis of variance (ANOVA) and their extensions, including testing, estimation, confidence interval procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. The theory will be approached mainly from the frequentist perspective and use of the computer (mostly R) to analyze data will be emphasized.
Prerequisite: Math 3200, a course in linear algebra (Math 309 or 429); some acquaintance with fundamentals of computer programming (CSE 131) and Math 493, or permission of instructor. 

  01  MWF  11:00A12:00P  Psychology / 249  FigueroaLopez  Dec 18 2018 10:30AM  12:30PM  40  40  13   

  01  MWF  3:00P4:00P  Cupples II / 230  Wickerhauser  Dec 13 2018 6:00PM  8:00PM  50  37  0   

  01  MWF  10:00A11:00A  Psychology / 249  FigueroaLopez  Dec 17 2018 10:30AM  12:30PM  35  28  0   

  01  MWF  12:00P1:00P  Duncker / 101  Syring  Dec 19 2018 10:30AM  12:30PM  50  39  0   

  01  MWF  2:00P3:00P  Rebstock / 215  Johnson  Dec 17 2018 3:30PM  5:30PM  75  70  0   

  01  TBA   TBA  Beheshti Zavareh  No final  999  0  0   
 02  TBA   TBA  Blank  No final  999  0  0   
 04  TBA   TBA  Ding  No final  999  0  0   
 05  TBA   TBA  Feres  No final  999  0  0   
 06  TBA   TBA  FigueroaLopez  No final  999  0  0   
 07  TBA   TBA  Freiwald  No final  999  0  0   
 08  TBA   TBA  Kerr  No final  999  0  0   
 09  TBA   TBA  Knese  No final  999  0  0   
 10  TBA   TBA  Krantz  No final  999  0  0   
 11  TBA   TBA  Kuffner  No final  999  0  0   
 12  TBA   TBA  Kumar  No final  999  0  0   
 14  TBA   TBA  McCarthy  No final  999  0  0   
 15  TBA   TBA  Roberts  No final  999  0  0   
 16  TBA   TBA  Shapiro  No final  999  0  0   
 17  TBA   TBA  Shareshian  No final  999  0  0   
 18  TBA   TBA  Spitznagel  No final  999  0  0   
 19  TBA   TBA  Stern  No final  999  0  0   
 20  TBA   TBA  Tang  No final  999  1  0   
 21  TBA   TBA  Wick  No final  999  0  0   
 22  TBA   TBA  Wickerhauser  No final  999  0  0   
 23  TBA   TBA  Wright  No final  999  0  0   

