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
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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.
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Please answer c to f in the attached
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 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.
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 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.)
←
Chapter 9 Solutions
Intro Stats, Books a la Carte Edition (5th Edition)
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- 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 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_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 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_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
- Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right 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 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 91 76 Left Arm 175 170 146 147 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=+x. (Round to one decimal place as needed.) % 5 Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.) Submit test Copyright © 2022 Pearson Education Inc. All rights reserved. Terms of Use | Privacy Policy Permissions Contact Us 6: 6 G H 8 4- 7 B N a hp 8 76 146 JK M H N (?) 83°F 12 + [ insert see score past due see score 40) 10:26 PM 8/9/2022 prt sc…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 272.9 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.2 277.8 Height 1785.3 1771.2 253.0 259.2 278.7 258.0 274.1 261.8 1675.9 1646.2 1858.9 1709.6 1788.8 1737.0 + C 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 272.9 mm is mm. (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? A. The result is very different from the actual height of 1776 mm. B. The result is exactly the same as the actual height of 1776 mm. C. The result is close to the actual height of 1776 mm. D. The result does not make sense given the context of the data.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 101 100 93 75 Left Arm 177 171 148 146 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=+x. (Round to one decimal place as needed.) 5 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. = T 6 & 1' a pyright © 2022 Pearson Education Inc. All rights reserved. Terms of Use | Privacy Policy | Permissions Contact Us (...) + 8 √₁ 74 146 1) 1₁ () More 110 11 85°F + Next = insert 4) prt sc backspaarrow_forward
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