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Brawn versus brain How is the weight of an animal’s brain related to the weight of its body? Researchers collected data on the brain weight (in grams) and body weight (in Page Number: 813 Page Number: 814 kilograms) for 96species of mammals. The following figure is a scatterplot of the logarithm of brain weight against the logarithm of body weight for all 96species. The least-squares regression line for the transformed data is

logy=1.01+0.72logxlogy^=1.01+0.72logx

Based on footprints and some other sketchy evidence, some people believe that a large ape-like animal, called Sasquatch or Bigfoot, lives in the Pacific Northwest. Bigfoot’s weight is estimated to be about 127kilograms (kg). How big do you expect Bigfoot’s brain to be?

Short Answer

Expert verified

The expected brain weight is 334.734grams.

Step by step solution

01

Given Information

Given data:

02

Explanation

Substituting the value of xin the regression equation

logy=1.01+0.72log(127)

=2.5247

Taking each side of the result's exponential with a base of 10:

y=10logy

=102.5247

=334.734

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Most popular questions from this chapter

Women who are severely overweight suffer economic consequences, a study has shown. They have household incomes that are $6710less than other women, on average. The findings are from an eight-year observational study of 10,039randomly selected women who were 16-24years old when the research began. If the difference in average incomes is statistically significant, does this study give convincing evidence that being severely overweight causes a woman to have a lower income?

a. Yes; the study included both women who were severely overweight and women who were not.

b. Yes; the subjects in the study were selected at random.

c. Yes, because the difference in average incomes is larger than would be expected by chance alone.

d. No; the study showed that there is no connection between income and being severely overweight.

e. No; the study suggests an association between income and being severely overweight, but we can’t draw a cause-and-effect conclusion.

Could mud wrestling be the cause of a rash contracted by University of Washington students? Two physicians at the university’s student health center wondered about this when one male and six female students complained of rashes after participating in a mud-wrestling event. Questionnaires were sent to a random sample of students who participated in the event. The results, by gender, are summarized in the following table.

Here is some computer output for the preceding table. The output includes the observed counts, the expected counts, and the chi-square statistic.

The cell that contributes most to the chi-square statistic is

a. men who developed a rash.

b. men who did not develop a rash.

c. women who developed a rash.

d. women who did not develop a rash.

e. both (a) and (d).

Exercises T12.4–T12.8 refer to the following setting. An old saying in golf is “You drive for show and you putt for dough.” The point is that good putting is more important than long driving for shooting low scores and hence winning money. To see if this is the case, data from a random sample of 69 of the nearly 1000 players on the PGA Tour’s world money list are examined. The average number of putts per hole (fewer is better) and the player’s total winnings for the previous season are recorded and a least-squares regression line was fitted to the data. Assume the conditions for
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a. If the scatterplot of the sample data wasn’t perfectly linear.

b. If the distribution of earnings has an outlier.

c. If the distribution of earnings wasn’t approximately Normal.

d. If the earnings for golfers with small putting averages was much more variable than the earnings for golfers with large putting averages.

e. If the standard deviation of earnings is much larger than the standard deviation of putting average.

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a. 0.1248

b. 0.28

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e. The answer cannot be determined from the given information.

T12.12 Foresters are interested in predicting the amount of usable lumber they can harvest from various tree species. They collect data on the diameter at breast height (DBH) in inches and the yield in board feet of a random sample of 20 Ponderosa pine trees that have been harvested. (Note that a board foot is defined as a piece of lumber 12 inches by 12 inches by 1 inch.) Here is a scatterplot of the data.

a. Here is some computer output and a residual plot from a least-squares regression on these data. Explain why a linear model may not be appropriate in this case.

The foresters are considering two possible transformations of the original data: (1) cubing the diameter values or (2) taking the natural logarithm of the yield measurements. After transforming the data, a least-squares regression analysis is performed. Here is some computer output and a residual plot for each of the two possible regression models:

b. Use both models to predict the amount of usable lumber from a Ponderosa pine with diameter 30 inches.
c. Which of the predictions in part (b) seems more reliable? Give appropriate evidence to support your choice.

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