Typically when researching a business or academic issue, you would ask a question about something and form a hypothesis. You would then collect the data and test it to see if the null hypothesis (no difference) is supported or refuted. Then you would present the findings.
For the purposes of this exam, we are revising some of these steps to focus on the statistical testing and interpretation aspect of hypothesis testing.
The final exam in this course is a “practical exam” that spans Modules 8 and 9. In this Part I assignment, you will review a data set and construct a hypothesis statement. In the Module 9, Part II assignment, you will run the appropriate tests and interpret the results. The Final Practical Exam is worth 15% of your final grade.
Before you get started, review the Module 7 – Heads-Up: Final Practical Exam page for additional details.
Now, open and carefully examine the data on the STAT 211 Altimeter Error spreadsheet (XLSX).
Download STAT 211 Altimeter Error spreadsheet (XLSX).
Also, review the Exam Related Resources section of the Module 8 – Readings & Resources page to see demonstrations on how to run statistical tests in Excel.
Then, construct a hypothesis and address the following in a document.
Describe what you are comparing.
State your hypothesis in a sentence.
Indicate the appropriate t-test that you will use to evaluate your hypothesis.
State the alpha (α) level.
In Part I, you will examine a set of sample data, formulate a hypothesis, determine the appropriate t-test to run, and determine the alpha (
) level. In Part II, after input on Part I from your instructor, you will run the test and interpret the results. Take some time now to think about the exam.
In Part I, you will evaluate an aircraft altimeter data sample from different companies to determine if there are any differences in error. You have the following three options for running the appropriate t-test.
Option 1 – compare one brand against a hypothesized mean to determine if the brand is statistically different than the assumed norm. In this case, you would have to cite where the hypothesized population mean came from.
Option 2 – compare one brand against another to see if there are significant differences between them.
Option 3 – compare either altimeter error of ACME or Brand X before adjustment and calibration and after adjustment and calibration to see if there are any differences in the means (performance).
Based on the type of dependent variable data (ratio) and the type of question in the scenario, you will use a t-test. As you know, there are three different types of t-tests you can use:
One sample t-test (test for one population mean using Excel)
Two sample t-test (hypothesis test for two population means using Excel)
Paired t-test (compare before and after values for one type using Excel)