PRECALCULUS:GRAPHICAL,...-NASTA ED.
PRECALCULUS:GRAPHICAL,...-NASTA ED.
10th Edition
ISBN: 9780134672090
Author: Demana
Publisher: PEARSON
Expert Solution & Answer
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Chapter 7, Problem 71RE
Solution

a.

To calculate: The linear regression equation for the population of Hawaii.

The obtained matrix is y12.89x+967.9 .

Given Information:

The table is defined,

    Time (years)Hawaii (thousands)Idaho (thousands)
    1980965944
    199011081007
    200012121294
    201013601568

Calculation:

Consider the given table,

    Time (years)Hawaii (thousands)Idaho (thousands)
    1980965944
    199011081007
    200012121294
    201013601568

Use a graphing calculator to find the linear rgeression.

Step 1. Insert the table in calculator by using the table feature.

  PRECALCULUS:GRAPHICAL,...-NASTA ED., Chapter 7, Problem 71RE , additional homework tip  1

Step 2. Use the LinReg feature with first two list L1 and L2 o obtain the linear regression model for the population of Philadelphia.

  PRECALCULUS:GRAPHICAL,...-NASTA ED., Chapter 7, Problem 71RE , additional homework tip  2

And,

  PRECALCULUS:GRAPHICAL,...-NASTA ED., Chapter 7, Problem 71RE , additional homework tip  3

Therefore, the obtained equation is y12.89x+967.9 .

b.

To calculate: The linear regression equation for the population of Ladho.

The obtained model is y21.59x+979.4 .

Given Information:

The table is defined,

    Time (years)Hawaii (thousands)Ldaho (thousands)
    1980965944
    199011081007
    200012121294
    201013601568

Calculation:

Consider the given table,

    Time (years)Hawaii (thousands)Ldaho (thousands)
    1980965944
    199011081007
    200012121294
    201013601568

Use a graphing calculator to find the logestic model.

Step 1. Insert the table in calculator by using the table feature.

  PRECALCULUS:GRAPHICAL,...-NASTA ED., Chapter 7, Problem 71RE , additional homework tip  4

Step 2. Use the LinReg feature with first two list L1 and L2 obtain the linear regression model for the population of Ladho.

  PRECALCULUS:GRAPHICAL,...-NASTA ED., Chapter 7, Problem 71RE , additional homework tip  5

Hence, the obtained result is y21.59x+979.4 .

c.

To Graph: The models in parts (a) and (b) and use these models to explain when the populations of the two states were about the same.

The obtained graph is defined as,

  PRECALCULUS:GRAPHICAL,...-NASTA ED., Chapter 7, Problem 71RE , additional homework tip  6

Given Information:

The table is defined,

    Time (years)Hawaii (thousands)Ldaho (thousands)
    1980965944
    199011081007
    200012121294
    201013601568

Calculation:

Consider the given table,

    Time (years)Hawaii (thousands)Ldaho (thousands)
    1980965944
    199011081007
    200012121294
    201013601568

Use a graphing calculator to find the logestic model.

Step 1. Insert the table in calculator by using the table feature.

  PRECALCULUS:GRAPHICAL,...-NASTA ED., Chapter 7, Problem 71RE , additional homework tip  7

Step 2. Graph the both equations (a) and (b) then use the intersect feature to find the intersection point.

  PRECALCULUS:GRAPHICAL,...-NASTA ED., Chapter 7, Problem 71RE , additional homework tip  8

The  x -coordinates of the point of intersection is 10.2, and it is representing year 1990.

Hence, the obtained years is 1990.

d.

To determine: The models, which appears to be a better fit for the data and explain which model would you choose to make predictions beyond 2020

Hawaii is having a linear model and Idaho population is having exponential or logistic

Given Information:

The table is defined,

    Time (years)Hawaii (thousands)Ldaho (thousands)
    1980965944
    199011081007
    200012121294
    201013601568

Calculation:

Consider the given information,

The linear model seems to be a good fit for Hawaii as most points appear to be on the line which means that there is almost a constant change in population.

An exponential or a logistic model seems to be appropriate for Idaho since there is a sudden increase in population for the same year intervals which is from 1990 to 2000.

Hence, Hawaii is having a linear model and Idaho population is having exponential or logistic

Chapter 7 Solutions

PRECALCULUS:GRAPHICAL,...-NASTA ED.

Ch. 7.1 - Prob. 1ECh. 7.1 - Prob. 2ECh. 7.1 - Prob. 3ECh. 7.1 - Prob. 4ECh. 7.1 - Prob. 5ECh. 7.1 - Prob. 6ECh. 7.1 - Prob. 7ECh. 7.1 - Prob. 8ECh. 7.1 - Prob. 9ECh. 7.1 - Prob. 10ECh. 7.1 - Prob. 11ECh. 7.1 - Prob. 12ECh. 7.1 - Prob. 13ECh. 7.1 - Prob. 14ECh. 7.1 - Prob. 15ECh. 7.1 - Prob. 16ECh. 7.1 - Prob. 17ECh. 7.1 - Prob. 18ECh. 7.1 - Prob. 19ECh. 7.1 - Prob. 20ECh. 7.1 - Prob. 21ECh. 7.1 - Prob. 22ECh. 7.1 - Prob. 23ECh. 7.1 - Prob. 24ECh. 7.1 - Prob. 25ECh. 7.1 - Prob. 26ECh. 7.1 - Prob. 27ECh. 7.1 - Prob. 28ECh. 7.1 - Prob. 29ECh. 7.1 - Prob. 30ECh. 7.1 - Prob. 31ECh. 7.1 - Prob. 32ECh. 7.1 - Prob. 33ECh. 7.1 - Prob. 34ECh. 7.1 - Prob. 35ECh. 7.1 - Prob. 36ECh. 7.1 - Prob. 37ECh. 7.1 - Prob. 38ECh. 7.1 - Prob. 39ECh. 7.1 - Prob. 40ECh. 7.1 - Prob. 41ECh. 7.1 - Prob. 42ECh. 7.1 - Prob. 43ECh. 7.1 - Prob. 44ECh. 7.1 - Prob. 45ECh. 7.1 - Prob. 46ECh. 7.1 - Prob. 47ECh. 7.1 - Prob. 48ECh. 7.1 - Prob. 49ECh. 7.1 - Prob. 50ECh. 7.1 - Prob. 51ECh. 7.1 - Prob. 52ECh. 7.1 - Prob. 53ECh. 7.1 - 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Prob. 8QRCh. 7.3 - Prob. 9QRCh. 7.3 - Prob. 10QRCh. 7.3 - Prob. 1ECh. 7.3 - Prob. 2ECh. 7.3 - Prob. 3ECh. 7.3 - Prob. 4ECh. 7.3 - Prob. 5ECh. 7.3 - Prob. 6ECh. 7.3 - Prob. 7ECh. 7.3 - Prob. 8ECh. 7.3 - Prob. 9ECh. 7.3 - Prob. 10ECh. 7.3 - Prob. 11ECh. 7.3 - Prob. 12ECh. 7.3 - Prob. 13ECh. 7.3 - Prob. 14ECh. 7.3 - Prob. 15ECh. 7.3 - Prob. 16ECh. 7.3 - Prob. 17ECh. 7.3 - Prob. 18ECh. 7.3 - Prob. 19ECh. 7.3 - Prob. 20ECh. 7.3 - Prob. 21ECh. 7.3 - Prob. 22ECh. 7.3 - Prob. 23ECh. 7.3 - Prob. 24ECh. 7.3 - Prob. 25ECh. 7.3 - Prob. 26ECh. 7.3 - Prob. 27ECh. 7.3 - Prob. 28ECh. 7.3 - Prob. 29ECh. 7.3 - Prob. 30ECh. 7.3 - Prob. 31ECh. 7.3 - Prob. 32ECh. 7.3 - Prob. 33ECh. 7.3 - Prob. 34ECh. 7.3 - Prob. 35ECh. 7.3 - Prob. 36ECh. 7.3 - Prob. 37ECh. 7.3 - Prob. 38ECh. 7.3 - Prob. 39ECh. 7.3 - Prob. 40ECh. 7.3 - Prob. 41ECh. 7.3 - Prob. 42ECh. 7.3 - Prob. 43ECh. 7.3 - Prob. 44ECh. 7.3 - Prob. 45ECh. 7.3 - Prob. 46ECh. 7.3 - Prob. 47ECh. 7.3 - Prob. 48ECh. 7.3 - Prob. 49ECh. 7.3 - Prob. 50ECh. 7.3 - Prob. 51ECh. 7.3 - 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Prob. 105ECh. 7.3 - Prob. 106ECh. 7.3 - Prob. 107ECh. 7.3 - Prob. 108ECh. 7.3 - Prob. 109ECh. 7.3 - Prob. 110ECh. 7.4 - Prob. 1QRCh. 7.4 - Prob. 2QRCh. 7.4 - Prob. 3QRCh. 7.4 - Prob. 4QRCh. 7.4 - Prob. 5QRCh. 7.4 - Prob. 6QRCh. 7.4 - Prob. 7QRCh. 7.4 - Prob. 8QRCh. 7.4 - Prob. 9QRCh. 7.4 - Prob. 10QRCh. 7.4 - Prob. 1ECh. 7.4 - Prob. 2ECh. 7.4 - Prob. 3ECh. 7.4 - Prob. 4ECh. 7.4 - Prob. 5ECh. 7.4 - Prob. 6ECh. 7.4 - Prob. 7ECh. 7.4 - Prob. 8ECh. 7.4 - Prob. 9ECh. 7.4 - Prob. 10ECh. 7.4 - Prob. 11ECh. 7.4 - Prob. 12ECh. 7.4 - Prob. 13ECh. 7.4 - Prob. 14ECh. 7.4 - Prob. 15ECh. 7.4 - Prob. 16ECh. 7.4 - Prob. 17ECh. 7.4 - Prob. 18ECh. 7.4 - Prob. 19ECh. 7.4 - Prob. 20ECh. 7.4 - Prob. 21ECh. 7.4 - Prob. 22ECh. 7.4 - Prob. 23ECh. 7.4 - Prob. 24ECh. 7.4 - Prob. 25ECh. 7.4 - Prob. 26ECh. 7.4 - Prob. 27ECh. 7.4 - Prob. 28ECh. 7.4 - Prob. 29ECh. 7.4 - Prob. 30ECh. 7.4 - Prob. 31ECh. 7.4 - Prob. 32ECh. 7.4 - Prob. 33ECh. 7.4 - Prob. 34ECh. 7.4 - Prob. 35ECh. 7.4 - Prob. 36ECh. 7.4 - Prob. 37ECh. 7.4 - 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