A manager at a local bank analyzed the relationship between monthly salary (y, in $) and...

 

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A manager at a local bank analyzed the relationship between monthly salary (y, in $) and length of service (x, measured in months) for 30 employees. She estimates the model: Salary = Bo +B1Service + ɛ. The following ANOVA table summarizes a portion of the regression results. df S MS Regression Residual 1 555,420 555,420 7.64 27 1,962,873 72,699 Total 28 2,518,293 Coefficients Standard Error t-stat p-value Intercept Service 784.92 322.25 2.44 0.02 9.19 3.20 2.87 0.01 The coefficient of determination indicates that, 22.06% of the variation in Salary is explained by the sample regression equation O 22.06% of the variation in Service is explained by the sample regression equation 81.61% of the variation in Salary is explained by the sample regression equation 81.61% of the variation in Service is explained by the sample regression equation A manager at a local bank analyzed the relationship between monthly salary (y, in $) and length of service (x, measured in months) for 30 employees. She estimates the model: Salary = Bo +B1Service + ɛ. The following ANOVA table summarizes a portion of the regression results. df S MS Regression Residual 1 555,420 555,420 7.64 27 1,962,873 72,699 Total 28 2,518,293 Coefficients Standard Error t-stat p-value Intercept Service 784.92 322.25 2.44 0.02 9.19 3.20 2.87 0.01 The coefficient of determination indicates that, 22.06% of the variation in Salary is explained by the sample regression equation O 22.06% of the variation in Service is explained by the sample regression equation 81.61% of the variation in Salary is explained by the sample regression equation 81.61% of the variation in Service is explained by the sample regression equation

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