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Annual high temperatures in a certain location have been tracked for several years. Let and Y the high temperature. Based on the data shown below, calculate the regression line (each value to two decimal places). y = X + 17.36 4. 17.88 18.6 6. 23.12 7 24.04 8 24.46 9. 25.68 10 29.7 11 31.62

Annual high temperatures in a certain location have been tracked for several years. Let and Y the high temperature. Based on the data shown below, calculate the regression line (each value to two decimal places). y = X + 17.36 4. 17.88 18.6 6. 23.12 7 24.04 8 24.46 9. 25.68 10 29.7 11 31.62

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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**Annual High Temperatures Regression Analysis** Annual high temperatures in a certain location have been tracked for several years. Let \( X \) represent the year and \( Y \) the high temperature. Based on the data shown below, calculate the regression line (each value to two decimal places). \[ y = \Box \times + \Box \] **Data Table:** | \( x \) | \( y \) | |--------|--------| | 3 | 17.36 | | 4 | 17.88 | | 5 | 18.6 | | 6 | 23.12 | | 7 | 24.04 | | 8 | 24.46 | | 9 | 25.68 | | 10 | 29.7 | | 11 | 31.62 | **Instructions:** To find the regression line, use the given data points to determine the best-fit line equation. This typically involves calculating the slope (\( m \)) and the y-intercept (\( b \)) using statistical formulas. Once determined, input your results in the equation format \( y = mx + b \).
Transcribed Image Text:**Annual High Temperatures Regression Analysis** Annual high temperatures in a certain location have been tracked for several years. Let \( X \) represent the year and \( Y \) the high temperature. Based on the data shown below, calculate the regression line (each value to two decimal places). \[ y = \Box \times + \Box \] **Data Table:** | \( x \) | \( y \) | |--------|--------| | 3 | 17.36 | | 4 | 17.88 | | 5 | 18.6 | | 6 | 23.12 | | 7 | 24.04 | | 8 | 24.46 | | 9 | 25.68 | | 10 | 29.7 | | 11 | 31.62 | **Instructions:** To find the regression line, use the given data points to determine the best-fit line equation. This typically involves calculating the slope (\( m \)) and the y-intercept (\( b \)) using statistical formulas. Once determined, input your results in the equation format \( y = mx + b \).
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