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For a specific species of fish in a pond, a wildlife biologist wants to build a regression equation to predict the weight of a fish based on its length. The biologist collects a random sample of this species of fish and finds that the lengths vary from 0.75 to 1.35 inches. The biologist uses the data from the sample to create a single linear regression model. Would it be appropriate to use this model to predict the weight of a fish of this species that is 3 inches long?
For a specific species of fish in a pond, a wildlife biologist wants to build a regression equation to predict the weight of a fish based on its length. The biologist collects a random sample of this species of fish and finds that the lengths vary from 0.75 to 1.35 inches. The biologist uses the data from the sample to create a single linear regression model. Would it be appropriate to use this model to predict the weight of a fish of this species that is 3 inches long?a. Yes, because 3 inches falls above the maximum value of lengths in the sample.
b. Yes, because the regression equation is based on a random sample.
c. Yes, because the association between length and weight is positive.
d. No, because 3 inches falls above the maximum value of lengths in the sample.
e. No, because there may not be any 3-inch fish of this species in the pond.
The correct answer is: d. No, because 3 inches falls above the maximumvalueof lengths in thesample.Inlinear regression, the model is based on the relationship observed in the sample data. The biologist collected data on fish lengths ranging from 0.75 to 1.35 inches, so the model is only valid within that range.Extrapolatingthe model to predict theweightof a fish with a length of 3 inches would be beyond the observed range of the data. This can lead to unreliable predictions as the relationship between length and weight may not hold true for lengths beyond therangeof thesample.To know more aboutlinear regression, visit:brainly.com/question/32840471#SPJ11...
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