SECTION 2.4 Exercises

For Exercises 2.77 to 2.79, see page 101; and for 2.80, see page 102.

Question 2.81

2.81 What’s wrong?

Each of the following statements contains an error. Describe each error and explain why the statement is wrong.

  1. A negative relationship is always due to causation.
  2. A lurking variable is always a quantitative variable.
  3. If the residuals are all negative, this implies that there is a negative relationship between the response variable and the explanatory variable.

Question 2.82

2.82 What’s wrong?

Each of the following statements contains an error. Describe each error and explain why the statement is wrong.

  1. An outlier will always have a large residual.
  2. If we have data at values of x equal to 1, 2, 3, 4, and 5, and we try to predict the value of y at x = 2.5 using a least-squares regression line, we are extrapolating.
  3. High correlation implies causation.

Question 2.83

2.83 Predict the sales

You analyzed the past 10 years of sales data for your company, and the data fit a straight line very well. Do you think the equation you found would be useful for predicting next year’s sales? Would your answer change if the prediction was for sales five years from now? Give reasons for your answers.

Question 2.84

2.84 Older workers and income

The effect of a lurking variable can be surprising when cases are divided into groups. Explain how, as a nation’s population grows older, mean income can go down for workers in each age group but still go up for all workers.

Question 2.85

2.85 Marital status and income

Data show that married, divorced, and widowed men earn quite a bit more than men the same age who have never been married. This does not mean that a man can raise his income by getting married because men who have never been married are different from married men in many ways other than marital status. Suggest several lurking variables that might help explain the association between marital status and income.

104

Question 2.86

2.86 Sales at a farmers’ market

You sell fruits and vegetables at your local farmers’ market, and you keep track of your weekly sales. A plot of the data from May through August suggests a increase over time that is approximately linear, so you calculate the least-squares regression line. Your partner likes the plot and the line and suggests that you use it to estimate sales for the rest of the year. Explain why this is probably a very bad idea.

Question 2.87

2.87 Does your product have an undesirable side effect?

People who use artificial sweeteners in place of sugar tend to be heavier than people who use sugar. Does this mean that artificial sweeteners cause weight gain? Give a more plausible explanation for this association.

Question 2.88

2.88 Does your product help nursing-home residents?

A group of college students believes that herbal tea has remarkable powers. To test this belief, they make weekly visits to a local nursing home, where they visit with the residents and serve them herbal tea. The nursing-home staff reports that, after several months, many of the residents are healthier and more cheerful. We should commend the students for their good deeds but doubt that herbal tea helped the residents. Identify the explanatory and response variables in this informal study. Then explain what lurking variables account for the observed association.

Question 2.89

2.89 Education and income

There is a strong positive correlation between years of schooling completed x and lifetime earnings y for American men. One possible reason for this association is causation: more education leads to higher-paying jobs. But lurking variables may explain some of the correlation. Suggest some lurking variables that would explain why men with more education earn more.

Question 2.90

2.90 Do power lines cause cancer?

It has been suggested that electromagnetic fields of the kind present near power lines can cause leukemia in children. Experiments with children and power lines are not ethical. Careful studies have found no association between exposure to electromagnetic fields and childhood leukemia.13 Suggest several lurking variables that you would want information about in order to investigate the claim that living near power lines is associated with cancer.