About The Instructor
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: 1 (08/25/2014 - 12/13/2014)
Seats Available: 6 Seats
The following additional fees apply to this section:
Fully Online Fee
This course is designed to give the student a general understanding of statistical tools as they are used in making decisions. The examples and exercises emphasize interpretation and are drawn from a wide range of actual situations. Excel computer software is used to simplify complex computations. The first section covers analysis decision-making through the use of summarized descriptive data, both ungrouped and grouped. This includes the study of frequency distributions, measures of central tendency and measures of dispersion. This section then deals with decision-making based upon classical hypothesis testing. It includes the study of curve fitting, large and small sample theory, the z distribution and the student's distribution. This coverage provides the student with a working understanding of statistical inference as a guide for judgment and action. The second section deals with more complex situations in decision-making with presentation of analysis of variance and chi-square testing with one and two variables and multiple outcomes of each variable. Excel is employed to handle the more complex calculations. The third section introduces the concepts and theories of correlation and linear regression and how these are used in statistical inference.
Kathleen K. Donoghue, MS Ed.
Lecturer of Mathematics
John Jay 310