Intro Stats, Books a la Carte Edition (5th Edition)
5th Edition
ISBN: 9780134210285
Author: Richard D. De Veaux, Paul Velleman, David E. Bock
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Textbook Question
Chapter 9.4, Problem 1JC
Recall the regression example in Chapter 7 to predict hurricane maximum wind speed from central barometric pressure. Another researcher, interested in the possibility that global warming was causing hurricanes to become stronger, added the variable Year as a predictor and obtained the following regression: (Data in Hurricanes 2015)
Dependent variable is: Max. Winds (kn)
R-squared = 80.6 s = 8.13
| Variable | Coefficient |
| Intercept | 1032.01 |
| Central Pressure | –0.975 |
| Year | –0.00031 |
1. Interpret the R2 of this regression.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
Please provide a detailed explanation for the questions attached.
In a 2020 report, Lea, an employee at the Ministry of Health, discussed the relationship between mean
annual temperature (x) and the mortality rate (y) for a type of skin cancer (Melanoma) in women at a Health
Conference in Jamacia. The data below contains the mean annual temperature (in degrees Celsius) and
Mortality Index for Melanoma. Use the data presented on these two variables to answer the following
questions.
Mortality
Temperature
102.5 104.5 100.4 95.9 87.0 95.0 88.6 89.2
51.3 49.9 50.0 49.2 48.5 47.8 47.3 45.1
(i) Plot a scatter diagram for the data above
(ii) Find the equation of the least squares regression line
(iii) Insert the regression line on the scatter diagram
(iv) State the coordinates of the y-intercept and the value of the gradient or slope.
(v) Calculate the correlation coefficient and interpret the result.
(vi) Calculate the coefficient of determination and interpret the result.
(vi) Use the equation in (ii) to determine the mortality rate you would expect to…
Please answer parts d, e and f.
Chapter 9 Solutions
Intro Stats, Books a la Carte Edition (5th Edition)
Ch. 9.4 - Recall the regression example in Chapter 7 to...Ch. 9.4 - Prob. 2JCCh. 9.4 - Prob. 3JCCh. 9 - Housing prices The following regression model was...Ch. 9 - Candy sales A candy maker surveyed chocolate bars...Ch. 9 - Prob. 3ECh. 9 - Prob. 4ECh. 9 - Prob. 5ECh. 9 - Prob. 6ECh. 9 - Movie profits once more Look back at the...
Ch. 9 - Prob. 8ECh. 9 - Prob. 9ECh. 9 - More indicators For each of these potential...Ch. 9 - Interpretations A regression performed to predict...Ch. 9 - Prob. 12ECh. 9 - Prob. 13ECh. 9 - Scottish hill races Hill runningraces up and down...Ch. 9 - Prob. 15ECh. 9 - Candy bars per serving: calories A student...Ch. 9 - Prob. 17ECh. 9 - More hill races Here is the regression for the...Ch. 9 - Prob. 19ECh. 9 - Home prices II Here are some diagnostic plots for...Ch. 9 - Admin performance The AFL-CIO has undertaken a...Ch. 9 - GPA and SATs A large section of Stat 101 was asked...Ch. 9 - Prob. 23ECh. 9 - Breakfast cereals We saw in Chapter 7 that the...Ch. 9 - Breakfast cereals again We saw a model in Exercise...Ch. 9 - Prob. 26ECh. 9 - Hand dexterity Researchers studied the dexterity...Ch. 9 - Candy bars with nuts The data on candy bars per...Ch. 9 - Scottish hill races, men and women The Scottish...Ch. 9 - Scottish hill races, men and women climbing The...
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forward
- Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?arrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.1 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.0 278.0 253.1 258.8 279.0 258.0 274.4 262.2 Height 1785.3 1771.2 1675.9 1646.3 1859.2 1710.4 1789.2 1737.2 The regression equation is y=+x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 273.1 mm is mm. (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? OA. The result is exactly the same as the actual height of 1776 mm. OB. The result is very different from the actual height of 1776 mm. OC. The result is close to the actual height of 1776 mm. OD. The result does not make sense given the context of the data.arrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.1 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.0 278.0 252.7 259.0 278.9 257.8 274.1 262.3 Height 1785.0 1770.9 1676.3 1646.0 1859.3 1710.1 1789.3 1737.2 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) ←arrow_forward
- For Data Set 9 in Appendix B, “Bear Measurements,” we get this regression equation: Weight = -274 + 0.426 Length + 12.1 Chest Size, with R2 = 0.928. Interpret the multiple coefficient of determination – what does this value tell us?arrow_forwardListed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 102 94 80 79 Left Arm 177 172 143 143 143 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = 35.5+ 1.36 x. (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. H T 101 6 & 7 4 L' 101 √₁ √ 1. (,) Copyright ©2022 Pearson Education Inc. All rights reserved. Terms of Use | Privacy Policy | Permissions | Contact Us | U H J 8 K fio More P 87°F Next + X insert (4) prt sc backspace 7:50 PM 8/5/202 enter delet hoarrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.1 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.2 252.8 258.7 278.9 258.1 274.2 262.1 Height 1785.1 1771.3 1676.1 1646.1 1858.8 1710.1 1789.2 1737.4 The regression equation is y = + Ox. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.)arrow_forward
- Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7 Height 1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.7 mm is (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O B. The result is exactly the same as the actual height of 1776 mm. O C. The result is very different from the actual height of 1776 mm. O D. The result does not make sense given the context of the data. C mm.arrow_forwardWhen a linear regression for the growth of the number of employees in the health care and social assistance fields in Quebec from 2010 to 2014 is performed, the following values are determined: r=0.9948 r2=0.9897 These values indicate that a linear model is a good fit for the data. True Falsearrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.3 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.1 253.3 259.4 279.1 257.8 273.6 262.2 Height 1785.0 1771.2 1676.2 1646.2 1858.9 1710.2 1788.7 1736.6 The regression equation is y=+x. X. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 273.3 mm is mm. (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? OA. The result is very different from the actual height of 1776 mm. OB. The result is exactly the same as the actual height of 1776 mm. OC. The result is close to the actual height of 1776 mm. OD. The result does not make sense given the context of the data.arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw Hill
College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning
Big Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt
Trigonometry (MindTap Course List)TrigonometryISBN:9781337278461Author:Ron LarsonPublisher:Cengage Learning
Glencoe Algebra 1, Student Edition, 9780079039897...
Algebra
ISBN:9780079039897
Author:Carter
Publisher:McGraw Hill
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Big Ideas Math A Bridge To Success Algebra 1: Stu...
Algebra
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:Houghton Mifflin Harcourt
Trigonometry (MindTap Course List)
Trigonometry
ISBN:9781337278461
Author:Ron Larson
Publisher:Cengage Learning
Correlation Vs Regression: Difference Between them with definition & Comparison Chart; Author: Key Differences;https://www.youtube.com/watch?v=Ou2QGSJVd0U;License: Standard YouTube License, CC-BY
Correlation and Regression: Concepts with Illustrative examples; Author: LEARN & APPLY : Lean and Six Sigma;https://www.youtube.com/watch?v=xTpHD5WLuoA;License: Standard YouTube License, CC-BY