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Chapter 12: Q. 23 (page 764)

The slope 尾 of the population regression line describes

(a) the exact increase in the selling price of an individual unit when its appraised value increases by \(1000

(b) the average increase in the appraised value in a population of units when the selling price increases by \)51000.

(c) the average increase in selling price in a population of units when the appraised value increases by \( 1000.

(d) the average selling price in a population of units when a unit's appraised value is 0.

(e) the average increase in appraised value in a sample of 16 units when the selling price increases by \) 1000.

Short Answer

Expert verified

(c) the average increase in selling price in a population of units when the appraised value increases by $ 1000.

Step by step solution

01

Given Information

Need to find the slope of the population regression.

02

Explanation

In general: The slope of the population regression line is the average increase of y as x increases by 1.

Since y is the actual selling price in thousands of dollars and x is the appraised market value in thousands of dollars, the correct answer then has to be (c).

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Most popular questions from this chapter

In a clinical trial 30, patients with a certain blood disease are randomly assigned to two groups. One group is then randomly assigned the currently marketed medicine, and the other group receives the experimental medicine. Each week, patients report to the clinic where blood tests are conducted. The lab technician is unaware of the kind of medicine the patient is taking, and the patient is also unaware of which medicine he or she has been given. This design can be described as

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Here is a scatterplot of the data.

(a) The following graphs show the results of two different transformations of the data. Would an exponential model or a power model provide a better description of the relationship between body mass and abundance? Justify your answer.

(b) Minitab output from a linear regression analysis on the transformed data of log(abundance) versus log(body mass) is shown below. Give the equation of the least-squares regression line. Be sure to define any variables you use.

PredictorCoefSE CoefTPConstant1.95030.134214.530.000log(body-1.048110.09802-10.690.000mass)

S=0.423352R-Sq=83.3%R-Sq(adj)=82.5%

(c) Use your model from part (b) to predict the abundance of black bears, which have a body mass of 92.5kilograms. Show your work.

(d) A residual plot for the linear regression in part (b) is shown below. Explain what this graph tells you about how well the model fits the data.

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