/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Q6CQQ The following exercises are base... [FREE SOLUTION] | 91影视

91影视

The following exercises are based on the following sample data consisting of numbers of enrolled students (in thousands) and numbers of burglaries for randomly selected large colleges in a recent year (based on data from the New York Times).

Repeat the preceding exercise, assuming that the linear correlation coefficient is r= 0.997.

Short Answer

Expert verified

Thebest predicted number of burglaries, with an enrollment of 50 (thousand), is 123.3.

The predictednumber of burglaries is obtained using the regression equation by substituting the measure of x as 50.

Step by step solution

01

Given information

A table represents the number of enrolled students (in thousands) and the number of burglaries for randomly selected large colleges in recent years.

The linear correlation coefficient is\(r = 0.997\).

From Exercise 5, the regression equation is\(\hat y = 3.83 + 2.39x\).

02

Discuss the type of model

A regression model is good if it follows the criteria stated below:

  • The scatterplot shows linear pattern.
  • The correlation coefficient measure is significant.
  • Extrapolation is not done for predicting the values.

A good model predicts the measure using the regression equation, while the predicted value of a bad model is the mean of the sampled response variables.

03

Check the type of model

The scatterplot for the samples is described below:

  • Mark enrolment on the x-axis and burglaries on the y-axis.
  • Scale the axes as per the observations.
  • Mark the paired observations on the plot.

The resultant graph is as follows.

The correlation measure is significant and the value 50 is close to the sampled enrollment data. Thus,the model is good.

04

Determine the predicted value

Using the regression equation, thebest predicted number of burglaries with an enrollment of 50 (thousand) is

\(\begin{array}{c}\hat y = 3.83 + \left( {2.39 \times 50} \right)\\ = 123.3.\end{array}\)

Therefore,thebest predicted number of burglaries with an enrollment of 50 (thousand) is 123.3.

The predicted number of burglaries for the enrollment of 50 (thousand) is obtained by substituting 50 for x in the provided regression equation.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91影视!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

let the predictor variable x be the first variable given. Use the given data to find the regression equation and the best predicted value of the response variable. Be sure to follow the prediction procedure summarized in Figure 10-5 on page 493. Use a 0.05 significance level.

Heights (cm) and weights (kg) are measured for 100 randomly selected

adult males (from Data Set 1 鈥淏ody Data鈥 in Appendix B). The 100 paired measurements yield\(\bar x = 173.79\)cm,\(\bar y = 85.93\)kg, r= 0.418, P-value = 0.000, and\(\hat y = - 106 + 1.10x\). Find the best predicted value of\(\hat y\)(weight) given an adult male who is 180 cm tall.

Exercises 13鈥28 use the same data sets as Exercises 13鈥28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.

Using the listed old/new mpg ratings, find the best predicted new

mpg rating for a car with an old rating of 30 mpg. Is there anything to suggest that the prediction is likely to be quite good?

Cigarette Nicotine and Carbon Monoxide Refer to the table of data given in Exercise 1 and use the amounts of nicotine and carbon monoxide (CO).

a. Construct a scatterplot using nicotine for the xscale, or horizontal axis. What does the scatterplot suggest about a linear correlation between amounts of nicotine and carbon monoxide?

b. Find the value of the linear correlation coefficient and determine whether there is sufficient evidence to support a claim of a linear correlation between amounts of nicotine and carbon monoxide.

c. Letting yrepresent the amount of carbon monoxide and letting xrepresent the amount of nicotine, find the regression equation.

d. The Raleigh brand king size cigarette is not included in the table, and it has 1.3 mg of nicotine. What is the best predicted amount of carbon monoxide?

Tar

25

27

20

24

20

20

21

24

CO

18

16

16

16

16

16

14

17

Nicotine

1.5

1.7

1.1

1.6

1.1

1.0

1.2

1.4

Interpreting the Coefficient of Determination. In Exercises 5鈥8, use the value of the linear correlation coefficient r to find the coefficient of determination and the percentage of the total variation that can be explained by the linear relationship between the two variables.

Bears r = 0.783 (x = head width of a bear, y = weight of a bear)

Testing for a Linear Correlation. In Exercises 13鈥28, construct a scatterplot, and find the value of the linear correlation coefficient r. Also find the P-value or the critical values of r from Table A-6. Use a significance level of A = 0.05. Determine whether there is sufficient evidence to support a claim of a linear correlation between the two variables. (Save your work because the same data sets will be used in Section 10-2 exercises.)

Revised mpg Ratings Listed below are combined city-highway fuel economy ratings (in mi>gal) for different cars. The old ratings are based on tests used before 2008 and the new ratings are based on tests that went into effect in 2008. Is there sufficient evidence to conclude that there is a linear correlation between the old ratings and the new ratings? What do the data suggest about the old ratings?

Old

16

27

17

33

28

24

18

22

20

29

21

New

15

24

15

29

25

22

16

20

18

26

19

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.