This section was canceled as of 06/16/2014
Section will be taught totally online with no scheduled class meetings. Students must arrange for daily access to a computer and the Internet prior to the start of classes. Robert Morris labs are to be used only as a backup in special situations and may not be relied upon for extended periods of time. In addition to the Internet link, online classes have a large emphasis on email. All messages from the instructor and other information regarding online classes, including user ids, passwords, and login instructions will be sent to your Robert Morris University email account.
Visit http://rmu.blackboard.com/ for more information.
Session, Dates: 7 (06/09/2014 - 08/02/2014)
Seats Available: Canceled
The following additional fees apply to this section:
Fully Online Fee
This course prepares prospective MBA students to examine basic topics in business statistics that are prerequisite to quantitative and non-quantitative course in an MBA program. Topics include a variety of descriptive and inferential statistics and probability applications encountered in business decision-making. The topics discussed in descriptive statistics and probability include: level of measurement, sample characteristics, descriptive and probability applications, discrete and continuous random variables, games of chance, standard normal and binomial distributions, and sampling distributions. The inferential statistics topics include: estimation, hypothesis testing using z and t tests, chi-square goodness-of-fit, ANOVA, regression, and applications using control charts. Students enrolled in this course use CD-ROM materials to investigate many of these topics within a self-paced format. Students who benefit most from this course are those who have been away from quantitative courses for a period of time and whose algebraic and statistical skills need to be refreshed. Students with weak mathematical backgrounds are probably best served by enrolling in QS211 Statistics.
David G. Hudak, Ph.D.
Department Head, Mathematics
Professor of Actuarial Science and Mathematics
John Jay 313