24. to 27. You gather data showing that children who live near the Austin city airport tend to do more poorly in school than the average Austin schoolchild. After thinking about the problem, you realize that there are several models that could account for this correlation:

(A) The noise from the airport causes the children to be unable to sleep at night. Hence they are not as attentive during the day, and consequently do not learn as well.

(B) Families who live near the airport tend to be poorer, and children from poor families do not do as well in school as children from more well-to-do families.

(C) The students who live near the airport attend grammar school near the airport. Furthermore, the teachers at the grammar school located nearest the airport are continually leaving when positions appear at other schools in Austin. Consequently, this grammar school has the most inexperience teachers in the district, and inexperienced teachers are not as effective as are experienced teachers.

(D) The noise from the airport causes the children to have personality disorders. Children with personality disorders learn more slowly than average.

The following data sets are ones that you might gather to investigate which of the above models is correct. For each data set, circle any model that the data could conceivably refute (i.e. do not circle a model if there is no conceivable way that the given data could show it wrong).

24. Data showing the income of people living near the airport, and the average income in Austin, and additional data showing the correlation between nightly sleep and school performance.

25. Data on the correlation between income and personality disorders.

26. Data showing the relation between how close a child's home is to the airport, and how close their grammar school is to the airport.

27. Data on the correlation between the airport noise levels a child is exposed to at home, and their family's income.

28. to 30. You gather data showing that among all U.T. students, those majoring in business more frequently own a personal computer than those majoring in fine arts. However, after a little thought you realize that there are several alternative models that could account for this correlation:

(A) Model: Students majoring in business tend to have more well-to-do parents than do students majoring in fine arts. Well-to-do parents buy their offspring computers relatively more often.

(B) Model: Students majoring in business hold part-time jobs more often than do students majoring in fine arts. Because of this, business majors are more often able to afford a personal computer.

(C) Model: Personal computers are required to enroll in certain classes needed to obtain a business degree, but are not required for any classes needed for a fine arts degree. Because of this, more business majors own a personal computer than do fine arts majors.

(D) Model: The salesmen who work for Apple Computer decided that business majors were more likely to buy a personal computer than were fine arts majors. Because of this, these salesmen targeted their direct mail advertising campaign at business majors, ignoring fine arts majors. This has led to business majors owning more personal computers than do fine arts majors.

(E) Model: Business majors tend to be older than fine arts majors. Older people more frequently own a personal computer than do younger people.

The following data sets are ones that you might gather to investigate which of the above models is correct. For each data set, circle any model that the data could conceivably refute (i.e. do not circle a model if there is no conceivable way that the given data could show it wrong).

28. Data: Data showing the ages of all current business and fine arts majors.

29. Data: Graph showing what percent of the salesmen who work for Apple Computer are fine arts majors.

30. Data: Data showing the percent of business majors holding a part-time job.