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 273.3 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. (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.
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 273.3 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. (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.
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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![### Educational Exercise: Analyzing Foot Lengths and Heights in Males
This exercise involves finding the linear regression equation for a dataset where foot length (in mm) is used as the predictor (independent variable) to estimate the height (in mm) of males. The dataset provided includes foot lengths and corresponding heights:
#### Data Table:
- **Foot Length (mm):** 281.9, 278.1, 253.3, 259.4, 279.1, 257.8, 273.6, 262.2
- **Height (mm):** 1785.0, 1771.2, 1676.2, 1646.2, 1858.9, 1710.2, 1788.7, 1736.6
#### Tasks:
1. **Find the Regression Equation:**
- The regression equation is of the form:
\[
\hat{y} = ( \text{y-intercept} ) + ( \text{slope} ) \cdot x
\]
- Round the y-intercept to the nearest integer and the slope to two decimal places as needed.
2. **Predicting Height:**
- Compute the best predicted height for a male with a foot length of 273.3 mm.
- Round the predicted height to the nearest integer.
3. **Comparison to Actual Height:**
- Compare your predicted height to an actual height of 1776 mm and determine the comparison:
- **Option A:** The result is very different from the actual height of 1776 mm.
- **Option B:** The result is exactly the same as the actual height of 1776 mm.
- **Option C:** The result is close to the actual height of 1776 mm.
- **Option D:** The result does not make sense given the context of the data.
Consider using statistical software or a calculator to perform the regression analysis to determine the specific values for the y-intercept and slope and to predict the height accurately.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fff4f609e-581a-443b-8b38-f9a882250427%2F0e25a71c-b995-4637-86c8-affc44024938%2Ftzbpxun_processed.jpeg&w=3840&q=75)
Transcribed Image Text:### Educational Exercise: Analyzing Foot Lengths and Heights in Males
This exercise involves finding the linear regression equation for a dataset where foot length (in mm) is used as the predictor (independent variable) to estimate the height (in mm) of males. The dataset provided includes foot lengths and corresponding heights:
#### Data Table:
- **Foot Length (mm):** 281.9, 278.1, 253.3, 259.4, 279.1, 257.8, 273.6, 262.2
- **Height (mm):** 1785.0, 1771.2, 1676.2, 1646.2, 1858.9, 1710.2, 1788.7, 1736.6
#### Tasks:
1. **Find the Regression Equation:**
- The regression equation is of the form:
\[
\hat{y} = ( \text{y-intercept} ) + ( \text{slope} ) \cdot x
\]
- Round the y-intercept to the nearest integer and the slope to two decimal places as needed.
2. **Predicting Height:**
- Compute the best predicted height for a male with a foot length of 273.3 mm.
- Round the predicted height to the nearest integer.
3. **Comparison to Actual Height:**
- Compare your predicted height to an actual height of 1776 mm and determine the comparison:
- **Option A:** The result is very different from the actual height of 1776 mm.
- **Option B:** The result is exactly the same as the actual height of 1776 mm.
- **Option C:** The result is close to the actual height of 1776 mm.
- **Option D:** The result does not make sense given the context of the data.
Consider using statistical software or a calculator to perform the regression analysis to determine the specific values for the y-intercept and slope and to predict the height accurately.
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