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North American population growth Many populations grow exponentially. Here are the data for the estimated population of North America (in millions) from 1700to 2012. The dates are recorded as years since 1700so that x=312is the year 2012.

year since1700population (in millions)025021007150262008225017229930730833731035131225

a. Use a logarithm to transform population size. Then calculate and state the least-squares regression line using the transformed variable.

b. Use your model from part (a) to predict the population size of North America in2020.

Short Answer

Expert verified

a). The least-squares regression line using the transformed variable is logy=0.1565+0.0079x.

b). The expected size of North America in 2020 is 483.615 million.

Step by step solution

01

Part (a) Step 1: Given Information

Given data:

year since1700population (in millions)0250210071502620017225030729933730834531035131232

02

Part (a) Step 2: Explanation

Log of given data:

year since1700population (in millions)log(population)020.3010299965020.30102999610070.84509804150261.414973348200821.9138138522503072.2355284472993372.4871383753083452.5276299013103512.5378190953122.545307116

Making use of a Ti83/84 calculator

Step 1: Select STAT;

Step 2: Select 1: EDIT

Step 3: Type the data for each year since 1700in list L1 and the population logarithmic in list L2.

Step 4: Hit STAT once more, select CALC, and then LinReg(a+bx).

03

Part (a) Step 3: Explanation

The required result:

y=a+bx

a=0.1565

b=0.0079

Substituting the value in aand b:

y=0.1565+0.0079x

Since 1700, xdenotes the year, while yis the population logarithm.

logy=0.1565+0.0079x
04

Part (b) Step 1: Given Information

Given data:

year since1700population (in millions)025021007150262008225017229930730833731035131225

05

Part (b) Step 2: Explanation

Substituting the value xby 320:

logy=0.1565+0.0079x

logy=0.1565+0.0079(320)

logy=2.6845

Using the exponential function with a base of ten

y=1012.6845

=102.6845

=483.615

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Most popular questions from this chapter

The swinging pendulum Refer to Exercise 33. We took the logarithm (base 10) of the values for both length and period. Here is some computer output from a linear regression analysis of the transformed data.


a. Based on the output, explain why it would be reasonable to use a power model to describe the relationship between the length and period of a pendulum.

b. Give the equation of the least-squares regression line. Be sure to define any variables you use.

c. Use the model from part (b) to predict the period of a pendulum with a length of 80cm.

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a. A plot of distance versus (time)2

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a.1500

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d.2500

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A scatterplot of yversus xshows a positive, nonlinear association. Two different transformations are attempted to try to linearize the association: using the logarithm of the y-values and using the square root of the y-values. Two least-squares regression lines are calculated, one that uses x to predict log(y) and the other that uses x to predict y. Which of the following would be the best reason to prefer the least-squares regression line that uses x to predict log(y)?

a. The value of r2is smaller.

b. The standard deviation of the residuals is smaller.

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d. The residual plot has more random scatter.

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