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Model R Square Adjusted R Square R. Std. Error of the Estimate .890 .783 .777 1.17580 ANOVA lodel Sum of df Mean Sig. Squares 206.153 Square Regression 3 68.718 49.705 000a Residual 53.918 39 1.383 Total 260.071 42 Coefficients Unstandardized Standardized Coefficients B Std. Error odel t Sig. Coefficients Beta 11.071 .000 .750 8.671 .000 352 4.321 .000 .037 386 .701 (Constant) 6.725 .607 persons per sa mi % pop below pov line .214 minority pop (%) .001 .000 .050 .006 015 ti-variable regression model is being used to investigate the relationship en various factors and the infant mortality rate in the US. Data is collected for states plus the Capital District. The model currently being explored is: mortality rate = f(population density, poverty, minority)

Model R Square Adjusted R Square R. Std. Error of the Estimate .890 .783 .777 1.17580 ANOVA lodel Sum of df Mean Sig. Squares 206.153 Square Regression 3 68.718 49.705 000a Residual 53.918 39 1.383 Total 260.071 42 Coefficients Unstandardized Standardized Coefficients B Std. Error odel t Sig. Coefficients Beta 11.071 .000 .750 8.671 .000 352 4.321 .000 .037 386 .701 (Constant) 6.725 .607 persons per sa mi % pop below pov line .214 minority pop (%) .001 .000 .050 .006 015 ti-variable regression model is being used to investigate the relationship en various factors and the infant mortality rate in the US. Data is collected for states plus the Capital District. The model currently being explored is: mortality rate = f(population density, poverty, minority)

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
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Model Summary Model Adjusted R Square R R Square Std. Error of the Estimate .890 .777 1.17580 1 .793 ANOVA Model Sum of df Mean F Sig. Squares 206.153 Square Regression 3 68.718 49.705 .000a Residual 53.918 39 1.383 Total 260.071 42 Coefficients Unstandardized Standardized Coefficients B Std. Error Model Sig. t Coefficients Beta 11.071 .000 .750 8.671 .000 .352 4.321 .000 386 .701 (Constant) 6.725 .607 persons per sq mi % pop below pov line .001 .000 214 .050 minority pop (%) .006 015 .037 A multi-variable regression model is being used to investigate the relationship between various factors and the infant mortality rate in the US. Data is collected for all 50 states plus the Capital District. The model currently being explored is: Infant mortality rate = f(population density, poverty, minority) ý = bo + bqX1 + bz%2 + byx3 x, is the population density, in persons per square mile; x2 is the percentage of the population that is below the poverty line x3 is the percentage of the population believed to belong to a visible minority group y-hat is the rate of deaths per 1000 infants. The outputs of this SPSS multiple regression model are given above. What is the fitted equation of this multiple regression model.
Transcribed Image Text:Model Summary Model Adjusted R Square R R Square Std. Error of the Estimate .890 .777 1.17580 1 .793 ANOVA Model Sum of df Mean F Sig. Squares 206.153 Square Regression 3 68.718 49.705 .000a Residual 53.918 39 1.383 Total 260.071 42 Coefficients Unstandardized Standardized Coefficients B Std. Error Model Sig. t Coefficients Beta 11.071 .000 .750 8.671 .000 .352 4.321 .000 386 .701 (Constant) 6.725 .607 persons per sq mi % pop below pov line .001 .000 214 .050 minority pop (%) .006 015 .037 A multi-variable regression model is being used to investigate the relationship between various factors and the infant mortality rate in the US. Data is collected for all 50 states plus the Capital District. The model currently being explored is: Infant mortality rate = f(population density, poverty, minority) ý = bo + bqX1 + bz%2 + byx3 x, is the population density, in persons per square mile; x2 is the percentage of the population that is below the poverty line x3 is the percentage of the population believed to belong to a visible minority group y-hat is the rate of deaths per 1000 infants. The outputs of this SPSS multiple regression model are given above. What is the fitted equation of this multiple regression model.
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