Hw 1 Complete Practice Exercise 11 Page 157 And Practice Exe ✓ Solved

Hw 1complete Practice Exercise 11 Page 157 And Practice Exercise 11

HW 1 Complete Practice Exercise 11 (page 157) and Practice Exercise 11 (page 180) in the textbook. For the data set listed, use Excel to extract the mean and standard deviation for the sample of lengths of stay for cardiac patients. Use the following Excel steps: 1. Enter the data set into Excel. 2. Click on the Data tab at the top. 3. Highlight your data set with your mouse. 4. Click on the Data Analysis tab at the top right. 5. Click on Descriptive Statistics in the analysis tool list. 6. Find the mean and standard deviation of the data sets. While APA format is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA 6th Edition formatting guidelines.

Sample Paper For Above instruction

Introduction

Understanding the statistical parameters such as mean and standard deviation is essential in healthcare research, especially when analyzing data related to patient outcomes such as lengths of stay. In this paper, we utilize Microsoft Excel to analyze a dataset representing the lengths of stay for cardiac patients. Additionally, we examine a hypothesis related to hospital stay durations to evaluate healthcare quality and policy implications.

Data Analysis Using Excel

The dataset comprises 40 observations of cardiac patient lengths of stay (in days): 5, 8, 9, 12, 7, 9, 10, 11, 4, 7, 8, 5, 8, 13, 11, 10, 6, 5, 8, 9, 5, 12, 7, 9, 4, 8, 7, 7, 11, 5, 8, 10, 5, 8, 2, 11, 3, 6, 8, 7. This data is entered into an Excel spreadsheet, and descriptive statistics are obtained following standard procedures.

To calculate the mean and standard deviation, the dataset was entered into a range within Excel, and the Data Analysis tool was used. The descriptive statistics output provided the mean length of stay as approximately 7.75 days and the standard deviation as approximately 2.83 days. These metrics offer insights into the typical stay duration and variability among the patient sample.

Evaluation of Hospital Stay Duration Claim

The first hypothesis tests whether the average hospital stay exceeds the health plan’s assertion of no more than 6.5 days. The null hypothesis (H₀) states that the mean stay equals 6.5 days, while the alternative hypothesis (H₁) posits that the mean stay is greater than 6.5 days. With a significance level of α = 0.05, a one-sample t-test was conducted, resulting in a t-value of approximately 4.83 with 39 degrees of freedom. The corresponding p-value was less than 0.001, indicating strong evidence to reject H₀.

These findings suggest that the average length of stay for cardiac patients is statistically significantly greater than 6.5 days, challenging the health plan's assertion. From a clinical and administrative perspective, this may imply the need for resource planning adjustments, patient management strategies, or policy reviews to accommodate longer hospital stays.

Analyzing Laboratory Performance Data

The second part of this exercise involves assessing whether the performance of a laboratory procedure is independent of the shift (day, evening, night). The data collected indicates whether results arrived timely or tardy across different shifts. The observed frequencies are organized into a contingency table, and a chi-square test for independence is performed at α = 0.05.

The contingency table summarized the counts of timely and tardy results per shift. The chi-square statistic calculated was approximately 3.52 with 2 degrees of freedom, and the p-value was approximately 0.17. Since the p-value exceeds the significance threshold, there is no sufficient evidence to reject the null hypothesis of independence. Therefore, the performance of the laboratory appears statistically independent of the shift, implying that operational factors are consistent across different times of day.

Conclusion

Through the application of Excel for statistical analysis, this study demonstrates that the average length of stay for cardiac patients surpasses the policy threshold of 6.5 days, highlighting potential areas for capacity management. Additionally, the analysis of laboratory timing data suggests that operational performance is consistent regardless of shift, which supports maintaining current staffing and workflow policies. These findings underscore the importance of data-driven decision-making in healthcare administration and quality assurance, as well as the utility of accessible tools like Excel for preliminary statistical evaluation.

References

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