Applied Mathematics in Business MATH M305 J691  BUS K305 J953  11:00 am - 12:15 pm TR, Hillside Hall 113

Text or readings:  to be announced (there will be no textbook for this course).

Course structure:  Students will consult with a client (a local business or agency) and complete a written project to solve a problem for the client or analyze data for the client.  The project will be presented in class and also to the client.  Students will work in small groups on the projects.  Projects will involve mathematical techniques such as forecasting, optimization, or queueing theory; students will use spreadsheets (Excel) to work on the client's problem and also to learn about the underlying mathematical tools.   There will be written homework and also two tests.  This is an experimental course whose development is being supported by the National Science Foundation and by Indiana University.  The goal of the course is for students to gain confidence using mathematical tools to solve interesting problems in familiar contexts, and also to gain experience in teamwork and working with clients.

Course grades: (Tentative) The major portion of the grade will be determined by the project (written and oral), which will count 50%.  Written homework will be 25% and tests will be the remaining 25%.  Grades will be computed on a standard 90% A, 80% B, 70% C, 60% D scale.  Regular attendance is vital for success in this course because much of the grade will be dependent on work done in class; a poor attendance record may be considered in the determination of grades.

Note:  If you need accomodations because of a disability, if you have emergency medical information to share with us, or if you need special arrangements in case the building must be evacuated, please inform either of us as soon as possible.  (Please see either of us privately after class or in our office.)

Calendar for course.
 

Lecture notes:	Modeling   R. C. Buck Mathematical modeling Notes 7 Forecasting models Notes 1 Elementary probability theory Notes 8 Exponential smoothing Notes 2 Compound events Notes 9 Winter's method Notes 3 Expected values Notes 10 Causal methods Notes 4 Histograms, continuous distributions Notes 11 Multiple regression Notes 5 Poisson process, mean and variance  Notes 12 Categorical variables Notes 6 Gaussian distribution
Assignments:	Homework 1  Homework 2  Homework 3  Homework 4  Homework 5  Homework 6
Spreadsheets:	AutoInsur.xls   Cards.xls Chuck.xls   DiceIndep.xls Expon.xls   UnfairDie.xlsUnfair2.xls  TenCoins.xls   HomeData.xls  Test1.xls
	Milk.xls  Copy.xls Scanner.xls  Vwbug.xls Copy_sets.xls  Milk_se.xls Lawn.xls  Soda.xls Income.xls   Distcost.xls Magazine.xls  Carwash.xls Ford.xls
Study guides:	test 1   test 2