Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate 429" 184 173 14.57213 a. Predictors: (Constant), Attractiveness (%), Number of Years as a Model, Age (Years) ANOVA: Model Sum of Squares df Mean Square F Sig 1 Regression 10871.964 3 3623.988 17.066 000 Residual 48202.790 227 212.347 Total 59074.754 230 9. Dependent Variable: Salary per Day (E) b. Predictors: (Constant), Attractiveness (%), Number of Years as a Model, Age (Years) Coefficients" Standard ized Unstandardized Coefficie 95.0% Confidence Coefficients nts Interval for B Lower Upper Model B Std. Error Beta Sig Bound Bound 1 (Constant] 60.890 16.497 -3.691 000 93.396 28.384 Age ( Years) 6.23 1.411 942 4.418 .000 3.454 9.015 Number of Years 95 8 5.561 2.122 -.548 -2.621 .009 -9.743 -1.380 Model Attractiveness (%] -.196 152 -.083 -1.289 199 -.497 104 9. Dependent Variable: Salary per Day (E) What is the R - value of the model? b. What are the R- - values? c. What is the meaning of the R- - value in regression analysis? d. Was the model significant? How could you determine significance in a regression model? e. Which predictor(s) were significant in the model