  01  MWF  9:00A10:00A  Wilson / 214  Shareshian  Dec 14 2017 3:30PM  5:30PM  175  120  0   
 02  MWF  10:00A11:00A  Wilson / 214  Shareshian  Dec 14 2017 3:30PM  5:30PM  150  137  0   
 B  R  8:00A9:00A  (None) /  Payne  Default  none  25  0  0   

  01  MWF  9:00A10:00A  Brown / 100  BerchenkoKogan  Dec 15 2017 10:30AM  12:30PM  207  117  0   
 02  MWF  10:00A11:00A  Brown / 100  BerchenkoKogan  Dec 15 2017 10:30AM  12:30PM  207  171  0   
 03  MWF  12:00P1:00P  Rebstock / 215  Thornton  Dec 15 2017 10:30AM  12:30PM  195  131  0   
 04  MWF  1:00P2:00P  Rebstock / 215  Thornton  Dec 15 2017 10:30AM  12:30PM  195  129  0   
 G  T  9:00A10:00A  Eads / 116  Norton  Default  none  30  21  0   
 M  T  10:00A11:00A  Eads / 116  Norton  Default  none  30  30  0   
 N  T  10:00A11:00A  Eads / 215  Wang  Default  none  30  28  0   
 R  T  11:00A12:00P  Eads / 116  Norton  Default  none  30  13  0   
 S  T  11:00A12:00P  Eads / 215  Wang  Default  none  30  8  0   
 U  T  12:00P1:00P  Eads / 116  Castor  Default  none  30  27  0   
 Z  T  12:00P1:00P  Eads / 215  Wang  Default  none  30  8  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 

  01  MTWRF  11:00A12:00P  Cupples I / 115  Tang  Dec 19 2017 10:30AM  12:30PM  35  23  0   

 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 12, October 10, and November 14. 

  01  MWF  9:00A10:00A  Hillman / 60  Krantz  Dec 15 2017 10:30AM  12:30PM  100  75  0   
 02  MWF  11:00A12:00P  Wilson / 214  Krantz  Dec 15 2017 10:30AM  12:30PM  125  122  0   

 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 13, October 11, and November 15.
Prereqs: Math 131 

  01  MWF  9:00A10:00A  Brown / 118  Huo  Dec 14 2017 3:30PM  5:30PM  120  89  0   
 02  MWF  11:00A12:00P  Brown / 118  Huo  Dec 14 2017 3:30PM  5:30PM  140  127  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 13, October 11, and November 15. 

  01  MWF  9:00A10:00A  Hillman / 70  Roberts  Dec 14 2017 3:30PM  5:30PM  205  197  0   
 02  MWF  11:00A12:00P  Steinberg / 105  Roberts  Dec 14 2017 3:30PM  5:30PM  200  192  0   
 03  MWF  12:00P1:00P  Steinberg / 105  Roberts  Dec 14 2017 3:30PM  5:30PM  200  171  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 9, and November 13. 

  01  MWF  11:00A12:00P  Rebstock / 215  Shapiro  Dec 18 2017 10:30AM  12:30PM  110  98  0   
 02  MWF  1:00P2:00P  Brown / 118  Shapiro  Dec 18 2017 10:30AM  12:30PM  125  115  0   

  01  MWF  12:00P1:00P  Wilson / 214  Blank  Dec 20 2017 10:30AM  12:30PM  90  74  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 12, October 10, and November 14.
Prereqs: Math 132. Though Math 233 is not essential, it is recommended. 

  01  MWF  9:00A10:00A  Rebstock / 215  Spitznagel  Dec 14 2017 3:30PM  5:30PM  75  61  0   

  01  MWF  3:00P4:00P  Lopata Hall / 103  Gallardo Candela  Dec 14 2017 6:00PM  8:00PM  20  4  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  2  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  3  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   

  01  MWF  10:00A11:00A  Eads / 208  Wickerhauser  Dec 18 2017 10:30AM  12:30PM  30  3  0   

  01  MWF  10:00A11:00A  Simon / 018  Blank  Dec 18 2017 10:30AM  12:30PM  70  44  0   

 Description:  Analytic functions, elementary functions and their properties, line integrals, the Cauchy integral formula, power series, residues, poles, conformal mapping and applications. Prereq: Math 318, Math 308, or ESE 317, or permission of instructor. 

  01  TR  11:30A1:00P  Eads / 204  McCarthy  Dec 18 2017 1:00PM  3:00PM  30  12  0   

  01  MWF  1:00P2:00P  Crow / 205  Gallardo Candela  Dec 20 2017 1:00PM  3:00PM  20  10  0   

  01  MWF  12:00P1:00P  Crow / 206  Kerr  Dec 20 2017 10:30AM  12:30PM  65  34  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 and a course in linear algebra (such as Math 309 or 429); some acquaintance with fundamentals of computer programming (such as CSE 131 or CSE 200), or permission of instructor. 

  01  MWF  3:00P4:00P  Crow / 206  Stern  Dec 14 2017 6:00PM  8:00PM  55  49  0   

  01  MWF  1:00P2:00P  Duncker / 101  Stern  Dec 20 2017 1:00PM  3:00PM  55  52  0   

  01  MWF  12:00P1:00P  Brown / 118  Lin  Dec 20 2017 10:30AM  12:30PM  75  42  0   

  01  MWF  2:00P3:00P  Hillman / 70  Feres  Dec 18 2017 3:30PM  5:30PM  165  158  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  1  0   
 18  TBA   TBA  Spitznagel  No final  999  1  0   
 19  TBA   TBA  Stern  No final  999  0  0   
 20  TBA   TBA  Tang  No final  999  0  0   
 21  TBA   TBA  Wick  No final  999  0  0   
 22  TBA   TBA  Wickerhauser  No final  999  1  0   
 23  TBA   TBA  Wright  No final  999  0  0   

