HA535 Healthcare Statistics

Running Head: INFERENTIAL STATISTICS 1

INFERENTIAL STATISTICS 3

Inferential statistics in health care

Name

Institution

Inferential statistics are important as they are used for comparing the differences between treatment groups. Various inferential statistics are applicable in healthcare; however, each is appropriate for research. I intend to look at the regression analysis throughout this paper.

The study was conducted to predict the emergency patient volume at the Indianapolis 500 miles race. Therefore, the researcher conducted the research and expected to have some numbers at the end of the study. The research question addresses the problem by trying to find out the population of patients during the race. Additionally, the research held weather conditions into consideration during the investigation. For the methodology, data was collected from the National Oceanic and Atmospheric Administration (Bowdish, Cordell, Bock, & Vukov, 1992). The data was from the patients that were treated between 1983 to 1989 Race Days. This data was collected from the facility hospital. Also, the regression analysis was done using the weather factors and race characteristics as independent variables, and the number of patients used as the independent variable. To test the validity of the model, data from 1990 was used.

` The regressive analysis is beneficial as it shows the relationship between the dependent and the independent variable. Moreover, I found out that regression analysis is ideal for giving forecast and predictions (Gunst, 2018). I looked at the results of the study, and they were as follows, there was a substantial relationship between the dew point and the patient load. Unfortunately, from the result, I found no correlation between wind, sunshine, humidity, number of patients, and race characteristics.

I believe the results of the study were accurate and presents the real picture. Therefore, I recommend that researchers who aim to compare variable always to use the regression analysis as it is useful. Additionally, it gives predictions on the future or likely happenings.

References

Bowdish, G. E., Cordell, W. H., Bock, H. C., & Vukov, L. F. (1992). Using regression analysis to predict emergency patient volume at the Indianapolis 500 mile race. Annals of emergency medicine, 21(10), 1200-1203.

Gunst, R. F. (2018). Regression analysis and its application: a data-oriented approach. Routledge.