hey tutor, I am so confused about this, could you please solve it for me? thanks!

Recovery (4 points) An honours student is analysing data that tracks recovery times (total time spent in hospital) of male and female patients assigned to different post- operative treatment regimes ('Treatment'). They modelled recovery time as a function of sex and treatment, generating the following output: Call: Im(formula = Recovery_time ~ Sex + Treatment, data = recovery) Residuals: Min 1Q Median 3Q Max -6.8042 -1.1730 0.4398 1.0298 6.0070 Coefficients: Estimate Std. Error t value Pr(>/t) (Intercept) 6.2302 0.6573 9.478 2.19e-10* * * Sexmale 2.6954 0.9678 2.785 0.00933 ** TreatmentB 1.6674 1.2097 1.378 0.17863 TreatmentC 2.9740 1.1323 2.626 0.01364 * Signif. codes: 0 *** 0.001 (** 0.01 '* 0.05 !' 0.1 "1 Residual standard error: 2.545 on 29 degrees of freedom Multiple R-squared: 0.4436, Adjusted R-squared: 0.3861 F-statistic: 7.708 on 3 and 29 DF, p-value: 0.0006194 # # #### # # # # # # # # # ## Anova Table (Type Ill tests) Response: Recovery_time### ## Anova Table (Type Ill tests) Response: Recovery_time Sum Sq Df F value Pr(>F) Sex 50.235 1 7.7571 0.009328 * * Treatment 45.968 2 3.5491 0.041804 * Residuals 187.802 29 Signif. codes: 0 1** *1 0.001 1* * 0.01 "* 0.05 :0.1 ''1nt 12 Recovery time 9 10 11 8 female male recovery$Sex Question 19 (3 points) Summarize the results concerning the significance (or not) of the Sex and Treatment factors, and the directions of any effects. Explain why the results for Treatment differ in the two tables (i.e. above vs. below the #################)? Question 20 (1 point) From the information above, do you have any concerns about the model that they fit? Explain. (Examination of assumptions indicated no issues with respect to the residuals.)