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دا A Moving to another question will save this response. << Question 5 of 10 >>> Question 5 1 points Save Answer Consider a regression model which predicts annual income on the basis of years of education (years_ed) and years of age (years_age). Using the regression output below, write the regression equation using problem-specific variable names. You may round values to whole numbers. Coefficients Standardized Model 1 Unstandardized Coefficients B (Constant) -138671.684 Coefficients 95.0% Confidence Interval for B Std. Error Beta t Sig. Lower Bound Upper Bound 39682.261 -3.495 .003 -222393.937 -54949.431 years_ed 9588.893 2539.098 580 3.776 .002 4231.865 14945.922 years_age 2234.556 879.885 .390 2.540 .021 378.162 4090.951 a. Dependent Variable: annual income

دا A Moving to another question will save this response. << Question 5 of 10 >>> Question 5 1 points Save Answer Consider a regression model which predicts annual income on the basis of years of education (years_ed) and years of age (years_age). Using the regression output below, write the regression equation using problem-specific variable names. You may round values to whole numbers. Coefficients Standardized Model 1 Unstandardized Coefficients B (Constant) -138671.684 Coefficients 95.0% Confidence Interval for B Std. Error Beta t Sig. Lower Bound Upper Bound 39682.261 -3.495 .003 -222393.937 -54949.431 years_ed 9588.893 2539.098 580 3.776 .002 4231.865 14945.922 years_age 2234.556 879.885 .390 2.540 .021 378.162 4090.951 a. Dependent Variable: annual income

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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دا A Moving to another question will save this response. << Question 5 of 10 >>> Question 5 1 points Save Answer Consider a regression model which predicts annual income on the basis of years of education (years_ed) and years of age (years_age). Using the regression output below, write the regression equation using problem-specific variable names. You may round values to whole numbers. Coefficients Standardized Model 1 Unstandardized Coefficients B (Constant) -138671.684 Coefficients 95.0% Confidence Interval for B Std. Error Beta t Sig. Lower Bound Upper Bound 39682.261 -3.495 .003 -222393.937 -54949.431 years_ed 9588.893 2539.098 580 3.776 .002 4231.865 14945.922 years_age 2234.556 879.885 .390 2.540 .021 378.162 4090.951 a. Dependent Variable: annual income
Transcribed Image Text:دا A Moving to another question will save this response. << Question 5 of 10 >>> Question 5 1 points Save Answer Consider a regression model which predicts annual income on the basis of years of education (years_ed) and years of age (years_age). Using the regression output below, write the regression equation using problem-specific variable names. You may round values to whole numbers. Coefficients Standardized Model 1 Unstandardized Coefficients B (Constant) -138671.684 Coefficients 95.0% Confidence Interval for B Std. Error Beta t Sig. Lower Bound Upper Bound 39682.261 -3.495 .003 -222393.937 -54949.431 years_ed 9588.893 2539.098 580 3.776 .002 4231.865 14945.922 years_age 2234.556 879.885 .390 2.540 .021 378.162 4090.951 a. Dependent Variable: annual income
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