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# IHP 525 Milestone Two Table

A. Assess the collected data. Use this section to layout the source, parameters, and any limitations of your data. Specifically, you should:

1. Describe the key features of your data set. Be sure to assess how these features affect your analysis.

# Summary statistics for los (Length of stay):

Group by: gender

This data set helps in analyzing the observation related from the length of stay in the hospitals and among this patients, the data set shows that 65 are male and the remaining are female. Analyzing the male, their shortest time period was a single day while their longest duration way 17 days. However on the other hand, females took a shorter period of 3 days while the longest time period was 56 days. This shows that females take a longer stay as compared to men with MI.

Analyzing the other parameters, the mean, mode and the median of both male and female that stays in the hospitals and the analysis is as shown in the table above. This would probably give a skewed graph to the right since the mean>median. The standard deviation also shows that the length of stay are closer to the expected average while the case is not the same for the females as in this case it spread out and contains outliers (Lisitsyna et al., 2019).

Constructed on the data set presented, out of 100 MI patients, 65 were males, and 35 werefemales. Concurring to the data set, the minimum length of hospital stays after experiencing aheart attack/MI/Stroke for male patients the minimum length of the hospital was 1 day, and themaximum length of hospital stay was 17 days. For female patients, the minimum length ofhospital stay was 3 days, and the maximum was 56 days. Moreover, on average, the data setdemonstrates that male patients stayed in the hospital for 6.3 days status post-MI, whereasfemale patients stayed 7.8 days on average. Amongst those studied, male patients most oftenstayed in hospital for 5 days while female patients stayed for 4 days.

• Analyze the limitations of the data set you were provided and how those limitations might affect your findings. Justify your response.

In this case, there are several limitation and this also affects the results differently, this include the fact that the sample size is small, there is no stated reasons why females take longer stays. For this case it’s likely to skew the data. Taking a large sample size may bring up the idea and reasons behind longer stays and if age is a factor in this case. This also does not give a clear

picture since the sample size of the male and the female was not the same and the fact that some information regarding the patients is not disclosed which makes it impossible to know if there is any other reason associated with the longer stay and or if environmental condition in the hospital is a factor. All this limitations are likely to impact the results as maybe gender maybe one of the factors that needs to be considered when analyzing hospital stays of MI patients (Sall et al., 2017).

Furthermore, conferring to various supplementary research, male patients have been associatedinstead with shorter hospital stays. In contrast, female patients were associated with relativelylonger hospital stays status post-MI episodes (Saczyski et al., 2010). This identical information issubstantiated by descriptive statistics sequenced on provided data set within the table above.Conversely, one significant limitation identified provided data set does not provide any evidenceabout whether any medical management/ intervention techniques or procedures were performedon patients under study, either males or females. Therefore, indisputably be tantamount tolimitation. Therefore, a specific type of procedure executed can drastically affect an MI patient’shospital length of stay. A significant alternative limitation is the sample size, which wasdrastically very small (100). The sample size could, in fact, easily lead to significant bias and,therefore, an inaccurate conclusion.

# References

Lisitsyna, L. S., & Oreshin, S. A. (2019). Sampling and Analyzing Statistical Data to Predict the Performance of MOOC. In Smart Education and e-Learning 2019 (pp. 77-85).

Springer, Singapore.

Sall, J., Stephens, M. L., Lehman, A., & Loring, S. (2017). JMP start statistics: a guide to statistics and data analysis using JMP. Sas Institute.

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