Prey attracts predators Here is one way in which nature regulates the size of animal populations: high population density attracts predators, which remove a higher proportion of the population than when the density of the prey is low. One study looked at kelp perch and their common predator, the kelp bass. On each of four occasions, the researcher set up four large circular pens on sandy ocean bottoms off the coast of southern California. He randomly assigned young perch to 1of 4pens so that one pen had 10perch, one pen had 20perch, one pen had 40perch, and the final pen had 60perch. Then he dropped the nets protecting the pens, allowing bass to swarm in, and counted the number of perch killed after two hours. A regression analysis was performed on the 16data points using x=number of perch in pen and y=proportion of perch killed. Here is a residual plot and a histogram of the residuals. Check whether the conditions for performing inference about the regression model are met.

Short Answer

Expert verified

Normal, equal standard deviation, random, independent, and linear are some of the requirements for regression inferences.

Step by step solution

01

Given information

We have to explain that whether the state for performing inference about the regression model are met or not.

02

Simplification

Normal,equalstandarddeviation,random,independent,andlineararesomeoftherequirementsforregressioninferences.
Normal: satisfied; the rationale for this is that the16 young perch samples represented less than 10%of all young perches.
The reason for this is that the vertical distribution of dots in the residual figure is roughly the same everywhere, resulting in an equal standard deviation.
The explanation for this is that the individual was assigned to a pen at random. Because the sample of sixteen juvenile perches represents less than 10%of all young perches, independent: satisfied. Because there isn't enough significant curvature, linear is satisfied, and percent is the residual figure.
As a result, all states or requirements have been met.

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Most popular questions from this chapter

Multiple Choice Select the best answer for Exercises 23-28. Exercises 23-28 refer to the following setting. To see if students with longer feet tend to be taller, a random sample of 25students was selected from a large high school. For each student, x=footlength&y=heightwere recorded. We checked that the conditions for inference about the slope of the population regression line are met. Here is a portion of the computer output from a least-squares regression analysis using these data:

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Multiple Choice Select the best answer for Exercises 23-28. Exercises 23-28 refer to the following setting. To see if students with longer feet tend to be taller, a random sample of 25students was selected from a large high school. For each student, x=foot length and y=height were recorded. We checked that the conditions for inference about the slope of the population regression line are met. Here is a portion of the computer output from a least-squares regression analysis using these data:

Which of the following is the equation of the least-squares regression line for predicting height from foot length?

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c. height^=91.9766+3.0867(foot length) height^=91.9766+3.0867(foot length)

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e. height^=3.0867+6.47044(foot length)heiight^=3.0867+6.47044(foot length)

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