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Putting It Together: Time to Complete a Degree A researcher wanted to determine if the mean time to complete a bachelor’s degree was different depending on the selectivity of the first institution of higher education that was attended. The following data represent a random sample of 12th-graders who earned their degree within eight years. Probability plots indicate that the data for each treatment level are normally distributed.

(a) What type of observational study was conducted? What is the response variable?

(b) Find the sample mean for each treatment level.

(c) Find the sample standard deviation for each treatment level. Using the general rule presented in this chapter, does it appear that the population variances are the same?

(d) Use the time to degree completion for students first attending highly selective institutions to construct a 95% confidence interval estimate for the population mean.

(e) How many pairwise comparisons are possible among the treatment levels?

(f) Consider the null hypothesis . If we test this hypothesis using t-tests for each pair of treatments, use your answer from part (e) to compute the probability of making a Type I error, assuming that each test uses an  level of significance.

(g) Use the one-way ANOVA procedure to determine if there is a difference in the meantime to degree completion for the different types of initial institutions. If the null hypothesis is rejected, use Tukey’s test to determine which pairwise differences are significant using a familywise error rate of .

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Lower Your Cholesterol Researcher Francisco Fuentes and his colleagues wanted to determine the most effective diet for reducing LDL cholesterol, the so-called “bad” cholesterol, among three diets: (1) a saturated-fat diet: 15% protein, 47% carbohydrates, and 38% fat (20% saturated fat, 12% monounsaturated fat, and 6% polyunsaturated fat); (2) the Mediterranean diet: 47% carbohydrates, and 38% fat (10% saturated fat, 22% monounsaturated fat, and 6% polyunsaturated fat); and (3) the US National Cholesterol Education Program or NCEP-1 Diet: 10% saturated fat, 12% monounsaturated fat, and 6% polyunsaturated fat. Participants in the study were shown to have the same levels of LDL cholesterol before the study and were randomly assigned to one of the three diets, or treatment groups. After 28 days, their LDL cholesterol levels were recorded. The data in the following table are based on this study.

(a) Why is this study a completely randomized design?

(b) What is the response variable? What is the explanatory variable that is controlled and set at three levels?

(c) The participants were randomly assigned to one of three treatment groups. What is the purpose of randomization in this study?

(d) State the null and alternative hypotheses.

(e) Verify that the requirements to use the one-way ANOVA procedure are satisfied. Normal probability plots indicate that the sample data come from normal populations.

(f) Are the mean LDL cholesterol levels different at the  level of significance?

(g) Draw boxplots of the LDL cholesterol levels for the three groups to support the analytic results obtained in part (f).

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A study by the Organization for Economic Cooperation and Development (OECD) found that the average annual hours actually worked per worker has decreased in most of Western Europe in the first decade of the 2000s. Suppose an OECD-wide survey is conducted that found that 43% of the responding workers in the survey cited ‘less business, less work’ as the number one reason for this reduction in the annual working hours. Suppose you want to test this figure in the Netherlands to determine whether Dutch workers feel the same way. A random sample of 315 Dutch full-time workers whose work-week has been getting shorter is chosen. They are offered a selection of possible reasons for this reduction and 120 pick ‘less business, less work’. Use techniques presented in this chapter and an alpha of 0.05 to test to determine whether the 43% figure for Western Europe for this reason holds true in the Netherlands.

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Is there a difference in the proportion of construction workers who are under 35 years of age and the proportion of telephone repair people who are under 35 years of age? Suppose a study is conducted in Dundee, Scotland, using random samples of 338 construction workers and 281 telephone repair people. The sample of construction workers includes 297 people below 35 years of age and the sample of telephone repair people includes 192 people under that age. Use these data to construct a 90% confi dence interval to estimate the difference in proportions of people under 35 years of age among construction workers and telephone repair people.

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