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Suppose you were to collect data for each pair of variables. You want to make a scatterplot. Which variable would you use as the explanatory variable and which as the response variable? Why? What would you expect to see in the scatterplot? Discuss the likely direction, form, and strength. a. Apples: weight in grams, weight in ounces b. Apples: circumference (inches), weight (ounces) c. College freshmen: shoe size, grade point average d. Gasoline: number of miles you drove since filling up, gallons remaining in your tank

Suppose you were to collect data for each pair of variables. You want to make a scatterplot. Which variable would you use as the explanatory variable and which as the response variable? Why? What would you expect to see in the scatterplot? Discuss the likely direction, form, and strength. a. T-shirts at a store: price each, number sold b. Scuba diving: depth, water pressure c. Scuba diving: depth, visibility d. All elementary school students: weight, score on a reading test

If we assume that the conditions for correlation are met, which of the following are true? If false, explain briefly. a. A correlation of -0.98 indicates a strong, negative association. b. Multiplying every value of \(x\) by 2 will double the correlation. C. The units of the correlation are the same as the units of \(y\).

If we assume that the conditions for correlation are met, which of the following are true? If false, explain briefly. a. A correlation of 0.02 indicates a strong, positive association. b. Standardizing the variables will make the correlation \(0 .\) c. Adding an outlier can dramatically change the correlation.

A study finds that during blizzards, online sales are highly associated with the number of snow plows on the road; the more plows, the more online purchases. The director of an association of online merchants suggests that the organization should encourage municipalities to send out more plows whenever it snows because, he says, that will increase business. Comment.

Suppose you were to collect data for each pair of variables. You want to make a scatterplot. Which variable would you use as the explanatory variable and which as the response variable? Why? What would you expect to see in the scatterplot? Discuss the likely direction, form, and strength. a. When climbing mountains: altitude, temperature b. For each week: ice cream cone sales, air-conditioner sales c. People: age, grip strength d. Drivers: blood alcohol level, reaction time

Suppose you were to collect data for each pair of variables. You want to make a scatterplot. Which variable would you use as the explanatory variable and which as the response variable? Why? What would you expect to see in the scatterplot? Discuss the likely direction, form, and strength. a. Legal consultation time, cost b. Lightning strikes: distance from lightning, time delay of the thunder C. A streetlight: its apparent brightness, your distance from it d. Cars: weight of car, age of owner

A study of traffic delays in 68 U.S. cities found the following relationship between Total Delay (in total hours lost) and Mean Highway Speed: Is it appropriate to summarize the strength of association with a correlation? Explain.

A researcher investigating the association between two variables collected some data and was surprised when he calculated the correlation. He had expected to find a fairly strong association, yet the correlation was near 0 . Discouraged, he didn't bother making a scatterplot. Explain to him how the scatterplot could still reveal the strong association he anticipated.

The errors in predicting hurricane tracks (examined in this chapter) were given in nautical miles. A statutory mile is 0.86898 nautical mile. Most people living on the Gulf Coast of the United States would prefer to know the prediction errors in statutory miles rather than nautical miles. Explain why converting the errors to statutory miles would not change the correlation between Prediction Error and Year.