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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 273.2 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.3 253.1 259.3 279.2 257.6 273.9 262.1 Height 1785.3 1771.0 1675.7 1645.8 1858.9 1710.3 1788.7 1736.8

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 273.2 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.3 253.1 259.3 279.2 257.6 273.9 262.1 Height 1785.3 1771.0 1675.7 1645.8 1858.9 1710.3 1788.7 1736.8

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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The image contains a prompt related to regression analysis. It includes an unfinished regression equation and questions about interpreting the results. **Text:** The regression equation is \( \hat{y} = \Box + (\Box) x \). (Round the constant to the nearest integer as needed. Round the coefficient to two decimal places as needed.) The best predicted height of a male with a foot length of 273.3 mm is \(\Box\) 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 close to the actual height of 1776 mm. - **C.** The result is exactly the same as the actual height of 1776 mm. - **D.** The result does not make sense given the context of the data. **Explanation:** The text is asking for the completion and interpretation of a regression equation, which is typically used to predict an outcome variable (\( \hat{y} \)) based on one or more predictor variables (\( x \)). The equation requires the constant and the coefficient of \( x \) to be filled in. The task also involves rounding numerical values as instructed. The problem further asks for the prediction of a male's height given a specific foot length and compares this predicted value to an actual value of 1776 mm. Options A to D offer possible interpretations regarding the accuracy or relevance of the predicted result.
Transcribed Image Text:The image contains a prompt related to regression analysis. It includes an unfinished regression equation and questions about interpreting the results. **Text:** The regression equation is \( \hat{y} = \Box + (\Box) x \). (Round the constant to the nearest integer as needed. Round the coefficient to two decimal places as needed.) The best predicted height of a male with a foot length of 273.3 mm is \(\Box\) 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 close to the actual height of 1776 mm. - **C.** The result is exactly the same as the actual height of 1776 mm. - **D.** The result does not make sense given the context of the data. **Explanation:** The text is asking for the completion and interpretation of a regression equation, which is typically used to predict an outcome variable (\( \hat{y} \)) based on one or more predictor variables (\( x \)). The equation requires the constant and the coefficient of \( x \) to be filled in. The task also involves rounding numerical values as instructed. The problem further asks for the prediction of a male's height given a specific foot length and compares this predicted value to an actual value of 1776 mm. Options A to D offer possible interpretations regarding the accuracy or relevance of the predicted result.
**Foot Lengths and Heights of Males** Listed below are foot lengths (mm) and heights (mm) of males. The task is to find the regression equation, using foot length as the predictor variable (x). We aim to determine the best predicted height of a male with a foot length of 273.2 mm. We will then compare this predicted height to an actual height of 1776 mm. | Foot Length (mm) | Height (mm) | |------------------|-------------| | 281.9 | 1785.3 | | 278.3 | 1771.0 | | 253.1 | 1675.7 | | 259.3 | 1645.8 | | 279.2 | 1858.9 | | 257.6 | 1710.3 | | 273.9 | 1788.7 | | 262.1 | 1736.8 | For an educational understanding, consider performing the calculations to create the linear regression model and forecasting the height at the specified foot length using statistical software or methods. Subsequently, analyze the variance or error between predicted and actual heights.
Transcribed Image Text:**Foot Lengths and Heights of Males** Listed below are foot lengths (mm) and heights (mm) of males. The task is to find the regression equation, using foot length as the predictor variable (x). We aim to determine the best predicted height of a male with a foot length of 273.2 mm. We will then compare this predicted height to an actual height of 1776 mm. | Foot Length (mm) | Height (mm) | |------------------|-------------| | 281.9 | 1785.3 | | 278.3 | 1771.0 | | 253.1 | 1675.7 | | 259.3 | 1645.8 | | 279.2 | 1858.9 | | 257.6 | 1710.3 | | 273.9 | 1788.7 | | 262.1 | 1736.8 | For an educational understanding, consider performing the calculations to create the linear regression model and forecasting the height at the specified foot length using statistical software or methods. Subsequently, analyze the variance or error between predicted and actual heights.
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