/*! 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} Q27BSC Regression and Predictions. Exer... [FREE SOLUTION] | 91影视

91影视

Regression and Predictions. 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 diameter/circumference data, find the best predicted circumference of a marble with a diameter of 1.50 cm. How does the result compare to the actual circumference of 4.7 cm?

Short Answer

Expert verified

The regression equation is\(\hat y = - 0.00396 + 3.14x\).

The best predicted circumference of marble with a diameter of 1.50 cm is 4.7 cm.

Step by step solution

01

Given information

Values are given on three variables namely, diameter, circumference, and volume.

02

Calculate the mean values

Let x represent the diameter.

Let y represent thecircumference.

Themean value of xis given as,

\(\begin{array}{c}\bar x = \frac{{\sum\limits_{i = 1}^n {{x_i}} }}{n}\\ = \frac{{7.4 + 23.9 + .... + 9.7}}{8}\\ = 12.375\end{array}\)

Therefore, the mean value of x is 12.375.

Themean value of yis given as,

\(\begin{array}{c}\bar y = \frac{{\sum\limits_{i = 1}^n {{y_i}} }}{n}\\ = \frac{{23.2 + 75.1 + .... + 30.5}}{8}\\ = 38.888\end{array}\)

Therefore, the mean value of y is 38.888.

03

Calculate the standard deviation of x and y

The standard deviation of x is given as,

\(\begin{array}{c}{s_x} = \sqrt {\frac{{\sum\limits_{i = 1}^n {{{({x_i} - \bar x)}^2}} }}{{n - 1}}} \\ = \sqrt {\frac{{{{\left( {7.4 - 12.375} \right)}^2} + {{\left( {23.9 - 12.375} \right)}^2} + ..... + {{\left( {9.7 - 12.375} \right)}^2}}}{{8 - 1}}} \\ = 8.371\end{array}\)

Therefore, the standard deviation of x is 8.371.

The standard deviation of y is given as,

\(\begin{array}{c}{s_y} = \sqrt {\frac{{\sum\limits_{i = 1}^n {{{({y_i} - \bar y)}^2}} }}{{n - 1}}} \\ = \sqrt {\frac{{{{\left( {23.2 - 38.888} \right)}^2} + {{\left( {75.1 - 38.888} \right)}^2} + ..... + {{\left( {30.5 - 38.888} \right)}^2}}}{{8 - 1}}} \\ = 26.307\end{array}\)

Therefore, the standard deviation of y is 26.307.

04

Calculate the correlation coefficient

Thecorrelation coefficient is given as,

\(r = \frac{{n\left( {\sum {xy} } \right) - \left( {\sum x } \right)\left( {\sum y } \right)}}{{\sqrt {\left( {\left( {n\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}} \right)\left( {\left( {n\sum {{y^2}} } \right) - {{\left( {\sum y } \right)}^2}} \right)} }}\)

The calculations required to compute the correlation coefficient are as follows:

The correlation coefficient is given as,

\(\begin{array}{c}r = \frac{{n\left( {\sum {xy} } \right) - \left( {\sum x } \right)\left( {\sum y } \right)}}{{\sqrt {\left( {\left( {n\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}} \right)\left( {\left( {n\sum {{y^2}} } \right) - {{\left( {\sum y } \right)}^2}} \right)} }}\\ = \frac{{8\left( {5391.3} \right) - \left( {99} \right)\left( {311.1} \right)}}{{\sqrt {\left( {\left( {8 \times 1715.6} \right) - {{\left( {99} \right)}^2}} \right)\left( {\left( {8 \times 16942} \right) - {{\left( {311.1} \right)}^2}} \right)} }}\\ = 0.999999\end{array}\)

Therefore, the correlation coefficient is 0.999999.

05

Calculate the slope of the regression line

The slopeof the regressionline is given as,

\(\begin{array}{c}{b_1} = r \times \frac{{{s_Y}}}{{{s_X}}}\\ = 0.999999 \times \frac{{26.307}}{{8.371}}\\ = 3.143\end{array}\)

Therefore, the value of slope is 3.14.

06

Calculate the intercept of the regression line

The interceptis computed as,

\(\begin{array}{c}{b_0} = \bar y - {b_1}\bar x\\ = 38.888 - \left( {3.143 \times 12.375} \right)\\ = - 0.00396\end{array}\)

Therefore, the value of intercept is -0.004.

07

Form a regression equation

Theregression equationis given as,

\(\begin{array}{c}\hat y = {b_0} + {b_1}x\\ = - 0.004 + 3.14x\end{array}\)

Thus, the regression equation is \(\hat y = - 0.00396 + 3.143x\).

08

Analyze the regression model

Referring to exercise 27 of section 10-1,

1)The scatter plot shows a linear relationship between the variables.

2)The P-value is 0.000.

As the P-value is less than the level of significance (0.05), this implies the null hypothesis is rejected.

Therefore, the correlation is significant.

Referring to figure 10-5, the criteria for a good regression model are satisfied.

Therefore, the regression equation can be used to predict the value of y.

The best predicted circumference of marble with a diameter of 1.50 cm is computed as,

\(\begin{array}{c}\hat y = - 0.00396 + \left( {3.14 \times 1.50} \right)\\ = 4.70604\end{array}\)

Therefore, the best predicted circumference of marble with a diameter of 1.50 cm is 4.7 cm.

09

Compare the result with the actual circumference of 4.7 cm

The predicted circumference of marble with a diameter of 1.50 cm is the same as the actual circumference.

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

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.)

22. Crickets and Temperature A classic application of correlation involves the association between the temperature and the number of times a cricket chirps in a minute. Listed below are the numbers of chirps in 1 min and the corresponding temperatures in 掳F (based on data from The Song of Insects, by George W. Pierce, Harvard University Press). Is there sufficient evidence to conclude that there is a linear correlation between the number of chirps in 1 min and the temperature?

Actress

28

30

29

61

32

33

45

29

62

22

44

54

Actor

43

37

38

45

50

48

60

50

39

55

44

33

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.

Find the best predicted temperature at a time when a cricket

chirps 3000 times in 1 minute. What is wrong with this predicted temperature?

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.)

Lemons and Car Crashes Listed below are annual data for various years. The data are weights (metric tons) of lemons imported from Mexico and U.S. car crash fatality rates per 100,000 population (based on data from 鈥淭he Trouble with QSAR (or How I Learned to Stop Worrying and Embrace Fallacy),鈥 by Stephen Johnson, Journal of Chemical Information and Modeling, Vol. 48, No. 1). Is there sufficient evidence to conclude that there is a linear correlation between weights of lemon imports from Mexico and U.S. car fatality rates? Do the results suggest that imported lemons cause car fatalities?

Lemon Imports

230

265

358

480

530

Crash Fatality Rate

15.9

15.7

15.4

15.3

14.9

In Exercises 5鈥8, use a significance level of A = 0.05 and refer to theaccompanying displays.Garbage Data Set 31 鈥淕arbage Weight鈥 in Appendix B includes weights of garbage discarded in one week from 62 different households. The paired weights of paper and glass were used to obtain the XLSTAT results shown here. Is there sufficient evidence to support the claim that there is a linear correlation between weights of discarded paper and glass?

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.

Crickets and Temperature r = 0.874 (x = number of cricket chirps in 1 minute, y = temperature in 掳F)

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.