Course Objective

This course is a graduate-level review of quantitative modeling, differential calculus, probability theory, and statistical inferences. It is not designed as a remedial course or a first-exposure course to help students pass the validation exams.

The course is taught with the assumption that students have had the prerequisite courses in calculus and statistics, or have done self-study. It goes beyond the mechanical, rote-learning typical of many undergraduate quantitative courses. The objective is to help students gain better appreciation of the conceptual foundation as well as the creative application of these subjects.

The operating style of this course is also intended to help students transition to the learning model at the LBJ School. This includes emphases on (1) understanding and communicating concepts and their implications, not on computation or correct numerical answers; (2) interactive learning driven by student discussions, instead of lecture and note-taking; and (3) difficult, group-based, applied problem-solving, rather than individual-based, mechanical exercises.

A Special Note to Students Who Ned to Take the Validation Exams

• Attendance in this class does not in itself fulfill the quantitative prerequisites, but this syllabus serves as a guidepost for the proficiency expected in the validation exams (See Note).
• We strongly encourage you to study and do practice exercises (see Camp reserves) on your own prior to Camp. This course is a good forum to ask questions and to have concepts clarified.
• If you are faced with the choice between (1) taking a formal course during the second half of the summer and missing Camp, versus (2) self-study, attend this review course, and then attempt the validation exam: Our general recommendation is the latter option—as long as you can put in four to six weeks of self-study time to get the first exposure and do practice exercises.
  

Course Organization

This course uses three teaching formats . Topical presentations are held Monday, Wednesday, and Friday mornings. An evening lab session is held each Tuesday and Thursday to review material and to demonstrate the mechanics of computation. A Saturday problem-solving session is offered each week to discuss the thinking process required in problem sets. The four topical modules of this course will be covered as follows:

Reading Materials

Frank Budnick: Applied Mathematics for Business, Economics, and the Social Sciences. (4th Ed.) New York: McGraw Hill, 1994 (out of print, Available in Camp LBJ Electronic Reserves)

Sam Kachigan: Multivariate Statistical Analysis: A Conceptual Introduction. (2nd Ed.). New York: Radius Press, 1991 . (See Note)(available at commercial bookstores)

For students who would like to begin their review at a more elementary level, the following two books are recommended as starters:

Barbara Bleau: Forgotten Algebra: A Self-Teaching Reference Course. Hauppauge, NY: Barrons, 1994.

Lloyd Jaisingh: Statistics for the Utterly Confused. New York: McGraw Hill, 2000.