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Chapter 12: Q.1.3 (page 777)
Use this linear model to predict the U.S. population in . Show your work.
The regression line,.
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Students in a statistics class drew circles of varying diameters and counted how many Cheerios could be placed in the circle. The scatterplot shows the results.
The students want to determine an appropriate equation for the relationship between diameter and the number of Cheerios. The students decide to transform the data to make it appear more linear before computing a least-squares regression line. Which of the following single transformations would be reasonable for them to try?
I. Take the square root of the number of Cheerios.
II. Cube the number of Cheerios.
III. Take the log of the number of Cheerios.
IV. Take the log of the diameter.
(a) I and II
(b) I and III
(c) II and III
(d) II and IV
(e) I and IV
Some high school physics students dropped a ball and measured the distance fallen (in centimeters) at various times (in seconds) after its release. If you have studied physics, then you probably know that the theoretical relationship between the variables is distance. A scatterplot of the students’ data showed a clear curved pattern. A scatterplot of which of the following should have a roughly linear pattern?
(a) [time, ln(distance)]
(b) [ln(time), distance]
(c) [ln(time), ln(distance)]
(d) [ln(distance), time]
(e) [distance, ln(time)]
Suppose that the relationship between a response variable y and an explanatory variable x is modelled by . Which of the following scatterplots would approximately follow a straight line?
(a) A plot of y against x
(b) A plot of y against log x
(c) A plot of log y against x
(d) A plot of log y against log x
(e) None of (a) through (d)
In Chapter 3, we examined data on the body weights and backpack weights of a group of eight randomly selected ninth-grade students at the Webb Schools. Some Minitab output from least-squares regression analysis for these data is shown
1. What conditions must be met for regression inference to be appropriate?
The equation of the least-squares regression line for predicting selling price from appraised value is
(a)
(b)
(c)
(d)
(e)
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