Thetrue means of the delay time in the hospitals are assumed to beequal. The assumption is that the patients were selected from thesame population therefore, the common mean will be:
Thealternative hypothesis states that the means are not equal.
SS(TOT) = (11.2-12.6 +7.6-12.6 +19-12=6) =(-1.4 -5 +6.4)267.92
SSerror = 67.92/2 = 37.46
Fromthe analysis, the p-value is greater than the critical valuealpha=0.05 therefore, the null hypothesis is rejected. From thecalculations in the excel file, the mean value of the variousparameters vary and thus the variation is not by chance. Thealternative hypothesis is therefore, accepted (Broyles2006).
Thereis a significant difference in the treatment effects among thepatients in the three hospitals and this is indicated by the hugevariation in the mean value for each hospital and patients as well.The p-value is greater than 0.05 (critical value) and thus the nullhypothesis is rejected. The null hypothesis was that the hospitalshad equal mean values. The variations in the block effects are not bychance. Therefore, the alternative hypothesis is accepted(Broyles2006).
Thereis negative correlation between the number of patient’s visits andthe mile coverage. From the results, an increase in the mile coverageprompted patients to visit the health facility more times. On theother hand, the magnitude of correlation is small and this shows thatthere is little level of flexibility and variation in the data(Broyles2006).Correlation defines the association between two variables and can beused to determine the causation of a given occurrence. Therefore, tosome extent long distance affects the hospital visits.
Broyles, R. W.(2006). Fundamentalsof statistics in health administration.Sudbury, MA: Jones and Bartlett.