1. The document describes steps to analyze categorical flight data: create a contingency table, state the null and alternative hypotheses for a test of independence, calculate the test statistic and report the p-value.
2. It asks to interpret the p-value in everyday language and suggest another test of independence that could be performed on the data.
3. The data provided is flight records from Portland, OR to destinations in other states, with information like carrier, flight number, departure/arrival times. It appears to be testing the independence of carrier and destination using this data.
1) Create a contingency table (this is very easy in Statgraphics .docx
1. 1) Create a contingency table (this is very easy in
Statgraphics: Describe > Categorical Data > Crosstabulation)
and copy this table into your report. Use Carrier and Origin as
your rows and columns, respectively.
2) What are the null hypothesis and the alternative hypothesis
for a test of independence based on this table? For our purposes,
we’ll pretend this data set is a random sample of all recent
flights out of Oregon.
3) Calculate this test statistic, and report your p-value. You may
calculate this “by hand”, but it will be much faster and easier in
Statgraphics. In Statgraphics, Describe > Categorical
Data > Crosstabulation > make sure Tests of Independence is
checked. One of the output boxes presents the result for this
test. Copy that test result into your report (excluding
StatAdvisor's interpretation) into your report.
4) What does this p-value tell you about flight carrier and origin
airport, in everyday language.
5) Give an example of a different test of independence we could
perform using this data.
.
/
MDY