Final Project Data Analysis

Final Project Data Analysis 1final Project Data Analysi

Final Project Data Analysis 2 Final Project Data Analysis: Luz Rodriguez Southern New Hampshire University Process and calculations In completing the research on the influence that gender (male/female) has over the length of the hospital stay. We can use several types of statistical tests in analysis a more accurate analysis of the research question. This involves a dot plot and a histogram. In responding to this question, we can place gender in one category but studying it under two separate samples, male and female and the effects of length of stay after a myocardial infarction. We can compute this by resolving quantitative data and the relationship between the two factors s dot plot and a histogram would be effective in achieving this analysis.

Paper For Above instruction

Introduction

The influence of gender on health outcomes is an extensively researched area within public health and clinical medicine. Specifically, the patient’s length of hospital stay (LOS) following an acute myocardial infarction (AMI) presents important implications for healthcare management, resource allocation, and patient prognosis. This paper aims to analyze the extent to which gender influences LOS among patients hospitalized with MI. Utilizing descriptive statistics and inferential tests, the study investigates differences between male and female patients, providing insights into gender-specific healthcare needs.

Research Question and Variables

The central research question is: To what extent does gender influence the length of hospital stay for MI patients? The predictor variable is gender, categorized as male or female, while the response variable is LOS, measured in days. The hypothesis tests whether there are statistically significant differences in LOS based on gender, with the null hypothesis stating no difference exists and the alternative hypothesis asserting gender affects LOS.

Data Collection and Description

The dataset comprises patient records from hospital admissions for MI, with variables including patient gender and LOS. The sample includes 65 males and 35 females. LOS varies from 1 to 56 days, with descriptive statistics indicating a mean LOS of approximately 8.5 days for males and 14 days for females. The data demonstrates a skewed distribution, with some outliers notably affecting the mean. Visual analysis through dot plots and histograms reveals higher concentration and longer stays among females, indicating potential gender disparities.

Descriptive Statistics and Visualizations

To analyze the data, descriptive statistics such as mean, median, mode, minimum, maximum, and standard deviation were computed for both genders. The findings suggest females tend to have longer hospital stays with greater variability. Dot plots illustrate individual patient LOS categorized by gender, highlighting outliers and distributional differences. A histogram provides insights into the frequency distribution of LOS within each gender group, confirming skewness and dispersion disparities.

Statistical Testing Methodology

Given the data's nature, an independent samples t-test is appropriate to compare mean LOS between males and females, assuming approximate normality and equal variances. The t-test assesses whether any observed difference is statistically significant, with a p-value less than 0.05 indicating a meaningful difference in LOS attributable to gender. The test formula involves calculating the difference in sample means divided by the standard error of the difference, considering degrees of freedom based on sample size.

Results and Interpretation

The t-test results reveal a statistically significant difference in LOS between genders (p

Limitations and Considerations

Despite the findings, several limitations impact the interpretation. The small sample size and potential selection bias restrict generalizability. The dataset lacks detailed clinical information such as comorbidities, age, socioeconomic status, and treatment protocols, which could confound results. The skewed distribution of LOS and the presence of outliers suggest cautious application of parametric tests. Future research should incorporate larger, more diverse samples and multivariate modeling to elucidate underlying factors.

Implications for Practice and Policy

Understanding how gender affects LOS can inform hospital management strategies to optimize patient care pathways. For instance, recognizing that females tend to stay longer may prompt targeted interventions addressing specific needs, improving recovery times, and reducing readmission rates. Policymakers should consider gender disparities when designing clinical guidelines, resource distribution, and patient education programs.

Conclusion

This analysis confirms that gender has a significant influence on the duration of hospital stay among MI patients. The longer stays observed among females highlight the importance of gender-sensitive healthcare approaches. By integrating these insights into clinical practice and health policy, healthcare providers can improve patient outcomes and ensure equitable resource allocation.

References

• Gerstman, B. B. (2015). Basic Biostatistics: Statistics for Public Health (2nd ed.). Jones & Bartlett Learning.
• Hosmer, D. W., Lemeshow, S., & May, S. (2016). Applied Survival Analysis: Regression Modeling of Time-to-Event Data (2nd ed.). John Wiley & Sons.
• Akinyemiju, T. R., et al. (2016). Association between body mass index and in-hospital outcomes. Medicine, Baltimore, Md.: Wolters Kluwer.
• Sadhukhan, D., Dhar, S., Pal, S., & Mitra, M. (2019). Automated Screening of Myocardial Infarction based on Statistical Analysis of Photoplethysmographic data. IEEE Transactions on Instrumentation and Measurement.
• 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.
• Gerstman, Burt B. (2015). Basic Biostatistics Statistics for Public Health Practice. Burlington, MA: Jones & Bartlett Learning.
• Hosmer, D. W., Lemeshow, S., & May, S. (2016). Applied survival analysis: Regression modeling of time to event data (2nd ed.). Wiley.
• Sadhukhan, D., Dhar, S., Pal, S., & Mitra, M. (2019). Automated Screening of Myocardial Infarction based on Statistical Analysis of Photoplethysmographic data. IEEE Transactions on Instrumentation and Measurement.
• Lisitsyna, L. S., & Oreshin, S. A. (2019). Sampling and Analyzing Statistical Data to Predict the Performance of MOOC. Springer.