This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. Specifically, you will be introduced to statistics and how to summarize data and learn concepts of frequency, normal distribution, statistical studies, sampling, and confidence intervals.
While you will be introduced to some of the science of what is being taught, the focus will be on applying the methodologies. This will be accomplished through the use of Excel and data sets from many different disciplines, allowing you to see the use of statistics in very diverse settings. The course will focus not only on explaining these concepts, but also understanding the meaning of the results obtained.
Upon successful completion of this course, you will be able to:
â€¢ Summarize large data sets in graphical, tabular, and numerical forms.
â€¢ Understand the significance of proper sampling and why you can rely on sample information.
â€¢ Understand why normal distribution can be used in so many settings.
â€¢ Use sample information to infer about the population with a certain level of confidence about the accuracy of the estimations.
â€¢ Use Excel for statistical analysis.
This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.

From the lesson

Module 3: Sampling and Central Limit Theorem

You are charged with analyzing a market segment for your company. You and your team have figured out what variables you need to understand; you also have an idea what factors might be influencing these variables of interest. Now you are ready to do your analysis. But, wait! Where is the data? How do you begin to get the data? In this module we will review the means by which you can begin to produce data â€“ the concepts of sampling and Central Limit Theorem â€“ and will help you understand how to produce "good" sample data and why sample data will work.