Firstly, I picked up three variables (X1: average temperature in January, X2: average temperature in February, and X3: average temperature in March) and set dependant variable Y as number of days after end of February. So, if the blooming day is 03/25, Y equals to 25; and if the day is 04/08, Y becomes 31+8=39. I arranged monthly average air temperature data in 1961 – 2004, ran MS-Excel regression data analysis function and gets the multiple linear regression equation as follows': Y=0.886X1 – 1.910X2– 3.213X3 + 63.069 (R²= 0.81)
Firstly, I picked up three variables (X1: average temperature in January, X2: average temperature in February, and X3: average temperature in March) and set dependant variable Y as number of days after end of February. So, if the blooming day is 03/25, Y equals to 25; and if the day is 04/08, Y becomes 31+8=39. I arranged monthly average air temperature data in 1961 – 2004, ran MS-Excel regression data analysis function and gets the multiple linear regression equation as follows': Y=0.886X1 – 1.910X2– 3.213X3 + 63.069 (R²= 0.81)
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Can someone explain to me how this person set up the his multiple linear regression equation in excel when he ran a regression? I assume his X
![Firstly, I picked up three variables (\(X_1\): average temperature in January, \(X_2\): average temperature in February, and \(X_3\): average temperature in March) and set dependent variable \(Y\) as number of days after end of February. So, if the blooming day is 03/25, \(Y\) equals to 25; and if the day is 04/08, \(Y\) becomes 31+8=39.
I arranged monthly average air temperature data in 1961–2004, ran MS-Excel regression data analysis function and gets the multiple linear regression equation as follows:
\[
Y = 0.886X_1 - 1.910X_2 - 3.213X_3 + 63.069 \quad (R^2 = 0.81)
\]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3280c582-e5cc-45be-a05b-b61a47a3d565%2F7884c481-2b24-4a3e-9508-212583a1f258%2Faijgbib_processed.png&w=3840&q=75)
Transcribed Image Text:Firstly, I picked up three variables (\(X_1\): average temperature in January, \(X_2\): average temperature in February, and \(X_3\): average temperature in March) and set dependent variable \(Y\) as number of days after end of February. So, if the blooming day is 03/25, \(Y\) equals to 25; and if the day is 04/08, \(Y\) becomes 31+8=39.
I arranged monthly average air temperature data in 1961–2004, ran MS-Excel regression data analysis function and gets the multiple linear regression equation as follows:
\[
Y = 0.886X_1 - 1.910X_2 - 3.213X_3 + 63.069 \quad (R^2 = 0.81)
\]
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