An ever-present need in business is to compare two populations, such as sales of related products, different customer segments, or productivity of factory work shifts, to name a few. In this course, you will examine how to compare two population means. Just as there is a need to look at two populations, the same is true for larger groups. However, the process of comparing three or more population means is significantly different. You will investigate the comparison of multiple means, including the experiment designs to choose from and the three-step process to follow. Additionally, you will explore how hypothesis testing is used to make judgments about a population.
Many times, however, comparisons are needed on more than one variable, such as a survey given to two different audiences or a defect caused by different pieces of equipment. Lastly, in this course you will examine tests on two variables, having either two options or multiple options and identify the formulas used in these comparisons.
WHAT YOU'LL LEARN
Examine how to compare two population means
Identify the two types of data and the steps to follow for each type
Review the formulas used in your calculations and the tools needed to visualize results.
Practice population comparisons
Investigate the comparison of multiple means, including the experiment designs to choose from and the three-step process to follow
Define factorial designs and what situations require their us
Perform a summative exercise on comparing multiple means
Explore how hypothesis testing is used to make judgments about a population
Define both binomial and multinomial random variables
Identify the steps to take in two-option and multi-option situations
Identify the formulas used in these tests and perform your own testing
Examine tests on two variables, having either two options or multiple options
Identify the formulas used in these comparisons
Assess the relationship between variables and visualize your findings in Excel
Cindy van Es is professor of practice in the Dyson School of Applied Economics and Management. She has a PhD in statistics from Iowa State University, and joined Cornell in 1988. She teaches three courses in the undergraduate business program: Introductory Statistics, Business Statistics, and Impact Learning: South Africa. Her general area of interest is statistical education, with a focus on business applications and teaching through social justice examples.
She currently serves as director of Dyson’s Undergraduate Business Program. In this position, she provides strategic leadership and supervision on activities within the undergraduate program at the school, focusing specifically on implementation of the undergraduate curriculum and review of academic policies