TU Data and Results Verified

First post:

As we are learning in this course, we should always be wary of data and results that are presented to us as facts. This discussion on infomercials is a good example of one needing to be careful to not be too trusting of data and survey results presented about the popularity of the product by the infomercial. Of course, there are good products that come out of infomercials. For example, the My Pillow seems to be a quality product. But, the way it is presented, advertised, and sold seems gimmicky and the “studies show…” results do not make one feel like they are believable.

We need to read the fine print of the survey results so to understand how many were surveyed, what questions were asked, in what order were the questions asked, etc. In this week’s unit on Chi Square Testing, Prof. Mirabella points out that it is important to have a large enough quantity of observations for a test to have reliable results.

For example, if a Test of Independence is used to to test the relationship of two categorical variables, a survey could be done asking men and women whether they like the My Pillow product. This would test if one’s preference for the My Pillow is independent of their gender. The survey needs to have enough people in it so that the results are useful. If only five men and five women are asked, the observed results would not be reliable to apply to population.

Knowing that a lot of people were surveyed and what questions were asked would help me to be more convinced that the “studies show…” results might be true.

Second post:

After completing this course, I can envision a few different ways that statistics would be useful in the organization where I work. In a university setting, as this course has shown, statistical analysis aids in many different ways to evaluate potential and current students’ undergraduate and graduate paths. It can be used to determine grade expectations, course scheduling, enrollment forecasting, etc.

Categorical Data Analysis can be used to evaluate enrollment for incoming freshmen. This would help with planning to know how many classes and professors will need to be offered and hired, respectively, to manage the size of the class. Also, as participation-required events are planned throughout the academic year, this tool can also aid in determining if extra events must be offered to ensure students have the opportunity to complete the requirement of number of hours or events.

As the university plans to have a minimum number of students in the freshman class, Central Tendency tools should be used to evaluate the GPAs from the enrolling students. The university will want to know the GPA data to know if it will need to offer more prerequisite classes or classes for students who are not up to standard for a subject, like math, for course scheduling.

As a former middle and high school teacher, I could also see the application of Chi Square categorical analysis to aid in setting up the coursework, creating a point system for each grading category and assignment. As a teacher, you want to ensure the system is fair and you do not make it so hard that no one will pass the class.

Using statistical analysis to plan for funding, enrollment, grading, etc. is extremely useful in the university setting. Many different applications are available also within other departments of the university as well, like Human Resources.