Confidence Intervals ; Case Context Description

confidence intervals

The case focus in this description will be the pulse rate of 10 patients in a clinical facility. Particularly, the patients’ pulse rateswill be recorded at the same time and recorded for statistical comparison. The chart below represents the pulse rates of the patients recorded in the clinical facility.

x Pulse Rates (bpm)
1. 75
2. 60
3. 85
4. 78
5. 65
6. 86
7. 92
8. 78
9. 78
10. 83













Variable and Parameter Description

The proportion of the sample population chosen was 10 patients. In this, the sample was also the entire population focused on in this analysis. The mean of the pulse rates recorded was 78bpm. In this case, the mean was the value of all units of x (bpm) divided by the data set proportion (sample population)(Holmes, Illowsky & Dean, 2017).The variables, in this case, were the pulse rates of different patients recorded in the statistical analysis.

Reason to Create an Interval to Represent the True Value of Patients’ Parameters

I chose to create a confidence interval parameter to represent the true mean value of pulse rates recorded between all the patients who participated as members of the sample population. The confidence interval, in this case, is the interval estimate calculated from the statistics of the observed data in analysis and it might portray the true value of an undetermined population parameter (Quirk, 2016; Harmon, 2011). The true value data will help to ascertain the normal range of healthy pulse rates per minute of most persons/patients in the clinical sector. At the 95% confidence interval parameter, it will be possible to ascertain the range of patients who will be at the “safest” spot mark in terms of their bpm.

Changing Confidence Interval to 90% and 99%

Changing the confidence of the pulse rates of the patient to 99% will reduce the margin of error in computing the true mean of bpm in the statistical data collected. Subsequently, this will also elevate the level of confidence in defining the range of the true mean bpm in the featured statistic. Contrary, reducing the confidence level to 90% will increase the margin of error in computing the true mean of the bpms for the ten sample population participants. Also, a reduction to 90% confidence interval will decrease the confidence level in defining the optimal bpm for other patients in the clinical facilities.

Best Confidence Level Between 90%, 95%, and 99%

The best confidence level between the three featured proportionate will be the 99% confidence interval. Additionally, that will be because it will ensure that a very small margin of error is present in computing the true mean of the bpm in healthy persons. Subsequently, this will aid physicians in the identification of an abnormality in a patient and implementation of effective medical interventions to address such medical situations

How the Study Findings Be presentedTo the Clinical Facility’s Management

The data collected by the researcher can be presented to the persons in charge at the medical facility by using a chartthat illustrates thet Value Confidence Interval (µ) from theConfidence Interval for proportions (p)employed. For instance, in this case, such a chart based on the bpm collected from the patients can be presented as indicated by the diagram below. Using such a chart will make it easy to explain the findings of a confidence interval analysis. It will also make it easy for the targeted audience of the report to see the lower and upper limits of the patients’ bpm, which could important in the diagnosis and implementation of medical interventions to patients.

Figure 1: Diagram illustratingthe t Value Confidence Interval (µ) attained from theConfidence Interval Tabulation of the pulse rates recorded from the 10 members of the sample population


Maria Alvarado Response

Alvarado, in her project, has provided a comprehensive discussion of all the key points that needed to be covered in the week six assignments. First, her choice of data population to be used in her statistical analysis is perfect [walk-in policy at work], and it is a phenomenon that needs to be developed. Also, Alvarado correctly identifies the need mean value of the population by identifying it to be the average number of walk-ins in the targeted medical institution. Also, Alvarado was right in her conclusion that increasing the confidence levels to 90% would reduce the management’s likelihood of creating a new walk-in policy. Contrary, reducing the confidence level to 99% will increase the management’s confidence that developing the same policy would be the right thing for the improved performance of the medical facility.

Abraham Figueroa Response

Figueroa makes a perfect choice of a case example of elderly patients with Cerebral Atherosclerosis for his statistical analysis. In this case, Figueroa also correctly computes the mean and standard deviation of the targeted population. Also, Figueroa computes the confidence interval of the featured data at 95% correctly and also computes the 99% confidence interval of the same data. The computation is correctly and well placed on the statistical chart for easier comparison. Lastly, Figueroa provides an accurate description and definition of the confidence interval in his report and also explains how increasing the confidence interval of a data closer to the mean helps in improving the accuracy to the mean.







Harmon, M. (2011).Confidence Intervals in Excel – The Excel Statistical Master. Indian Rocks Beach, FL: Mark Harmon.

Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory Business Statistic. OpenStax. Retrieved from

Quirk, T. J. (2016).Excel 2016 for Business Statistics: A Guide to Solving Practical Problems. Basingstoke, England: Springer.

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