Use the regression analysis results from the previous question to match each metric below to its value. Intercept 9.0548 Slope -0.1468 F-value 0.6381 p-value 0.4283 R-squared 0.01312 Effect size [ Choose ] Coefficient of determination (%) 0.115 Coefficient of non-determination (%) 1.312 Variability in Y that can be explained by X (%) [ Choose ] Variability in Y that cannot be explained by X (%) [ Choose ]16 - 12- job_offers 8 - . 4 - 20.0 22.5 25.0 27.5 30.0 ageThe direction of the Slope indicates that there is a _________ relationship between age and job offers, which means that when age goes _________, job offers goes 0 positive, up. down O negative. up,down 0 positive, up. up 0 negative. up, up R R 4.2.1 . ~/LABS/ 1m(formula = job_offers ~ age, data = offers) Residuals : Min 10 Median 30 Max -4. 971 -2. 649 -1. 310 1. 837 10. 910 coefficients : Estimate Std. Error t value Pr (>|t/) (Intercept) 9. 0548 4. 5318 1. 998 0. 0514 age -0. 1468 0. 1838 -0. 799 0. 4283 signif. codes: 0 0. 001 '* * ' 0. 01 '* ' 0. 05 . ' 0.1 ' ' 1 Residual standard error: 3. 779 on 48 degrees of freedom Multiple R-squared: 0. 01312, Adjusted R-squared: -0. 007441 F-statistic: 0. 6381 on 1 and 48 DF, p-value: 0. 4283 > summary (offers . reg) $r . squared [1] 0. 01311944 > coef (offers. reg) (Intercept) age 9. 0548192 1468472