Ronya Bentz, Lasondra Defreeze, Terri Dougherty, Grace Zhao HCS/438
September 24, 2012
Visual Data Displays and Uses in Decision Making
Studying the measures of central tendency will help to verify if these measures of central tendency for the given data are correct. The information will assist in predicting specific health issues and interventions needed to improve health care. The measure of variation produces a conclusion through the Tele-care monitoring system. The types of central tendency conducted in this study were the mean and median. The description of data in this study uses the five-number summary. Variables were also used to predict key medical events and interventions, based on significance. According to Biddiss, Brownsell, Hawley (2009), “the data analysis was conducted using statistical software and logistic regression was used to predict the occurrence of key medical events/interventions taken from health care logs of health-care workers.” Biddiss, Brownsell, Hawley 2009’s articles explain examples in the text are as follows: The 45 patients studied a total of 8576 alerts were generated. A total of 171 medical events which included the mean number of medical events for the year which was 3.5, the median 2, and the quartile ranged between 1- 4. The mean average of key alerts per year was 49, with a median of 49, and an interquartile range of 47-51. The average percentage of total alerts that were medical events was 6.4% with a median of 4 and an interquartile range of 1.4-8 (p. 227-228). Because the focus of the study determined the average need for medical intervention in congestive heart failure, the use of the measure of central tendency is correct in this study. According to Bennett, Briggs, & Thiola, (2009), “variation is a measure of how much the data values are spread out. A distribution in which most data are clustered together has a low...