The following tables show results of a regression analysis between birth rate (per 1000 of population) and median age among a random sample of 30 states in the US. Follow a 5-step process to test if median age can significantly predict birth rate is (0=0.05). (5 marks) ANOVA Sum of Mean Model Squares df Square F Sig. Regression 20787.547 1 20787.547 113.473 000b Residual 5129.420 28 183.194 Total 25916.967 29 a. Dependent Variable: Birth rate b. Predictors: (Constant), Median age Coefficients Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta Sig 1 (Constant) 589.188 39.713 14.836 000 Median age -14.239 1.337 .896 -10.652 000 a. Dependent Variable: Birth rateMultiple Comparisons Dependent Variable: ROM Tukey HSD Mean Difference 95% Confidence Interval (1) Group (J) Group (1-J) Std. Error Sig Lower Bound Upper Bound Juvenile RA -6.181' 1.803 002 -10.43 -1.94 OA 1.337 1.911 764 -3.16 5.83 RA Juvenile 6.181" 1.803 002 1.94 10.43 OA 7.518 1.921 000 3.00 12.04 OA Juvenile -1.337 1.911 764 -5.83 3.16 RA -7.518" 1.921 000 -12.04 -3.00 * The mean difference is significant at the 0.05 level. Can we conclude that this is a difference in the ROM between the three patient populations? Explain your answer (1 mark) What sources of variation are considered in the computed F ratio? (1 mark) Based on the results from the post-hoc test, what patient groups differ with respect to their ROM? (1 mark) What patient group has the greatest ROM? Explain your answer (1 mark) What is the 95% confidence interval for the mean ROM of the patient group with the greatest ROM? (1 mark)