is closely corresponding to the approximation of df, where df=k-1. Thus, with the computed analysis, it is clear that one sample population does show a significant difference the other two. It can be assumed that Cycling is significantly different in terms of how many calories it burns compared to the other two sample groups. It is significantly lower in terms of how many calories it burns within the context in comparison to the other sampled activities of swimming and tennis. Swimming and Tennis are much closer, with less of a significant difference between them, showing much more correlation in regards to the amount of calories burned within the workout regime setting. Based on the analysis, however, it is clear hat Tennis burns the most calories out of the two listed activities with less of a significant difference. .
Question 3
Quality of Inpatient Treatment
In thus data set, forty patients represent the sample set to be used to determine the correlation between the number of visitations and perceived quality of the care based on the opinion of the patient. The patients were divided into visitor categories, in which 1=frequent, 2=occasional, and 3=rare. Then, treatment was valued between the scale of 1=good, 2=fair, and 3=poor. A Chi-square Test was then performed on the data set to determine if there was a significant difference between the number of visits and the perceived quality of care within the given set of surveyed patients.
Data Set
Chi-Square Test Results
Test Statistics
visitors treatment
Chi-Square
.050a
.650a
df
2
2
Asymp. Sig.
.975
.723
a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 13.3.
visitors
Observed N
Expected N
Residual
frequent
13
13.3
-.3
occasional
13
13.3
-.3
rare
14
13.3
.7
Total
40
treatment
Observed N
Expected N
Residual
good
11
13.3
-2.3
fair
14
13.3
.7
poor
15
13.3
1.7
Total
40
The
sample size for frequent patients and medium frequency patients was thirteen variables. The sample size for patients who rarely needed care within the
hospital setting was set at fourteen variables. Each was analyzed in comparison to each other utilizing the principle equations within the Chi-Square Test. It is clear here that the
null hypothesis was true in this case. Through the analysis, it was observed that there were significant differences between all three groups. With this analysis, it was clear that the higher duration spent in care signified a pre-determined rating of its
healthcare quality. Sample a, the patients who were frequently being cared for by
hospital staff showed a mean perceived care rating of 1.38. Sample B, patients who needed occasional care had a different mean of a 2.08 perceived care rating. Those who rarely entered the hospital setting showed a mean perceived rating as 2.86. Thus, the findings of the analysis shows that those patients who are need of care more often are less satisfied with the type of care they receive. This may be on the increased demands of these patients or the frustration of the frequency of visits. Whatever the base cause, it is clear that frequent patients are the most unsatisfied in terms of their perceived care. Patients who occasionally needed care had an improved perception of the hospital care setting, however, this rating was still set at fair. It was only Sample C, or the patients who rarely needed hospital care who were the most satisfied with the type of care they were receiving. Although not fully satisfied, this was the closest population of a satisfied care rating. This may be based on the notion that they are not used to typical care standards based on their low frequency of visits, and so do not know what negative elements to look for within their care setting. With this analysis, the hospital can now look to improve the care strategies used for more frequent or long-term patients, as this is the population who is the most dissatisfied, as well as the population most encountered within the day-to-day hospital setting.
