application using regression analysis for decision making
Regression analysis is a statistical tool that is used for two main purposes: descriiption and prediction.
Provide an example of an application using regression analysis for decision making in a hospital setting that involves either descriiption or prediction.
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There are different models of regression that are used in an analysis for decision-making in a hospital. Regression Analysis is a statistical process that estimates relationships of independent, criterion, dependent variables, and predictors. Regression Analysis has major uses for determining the strength of predictors, forecasting an effect, and trend forecasting. There are three types of major regression that are used commonly, which are linear, polynomial, and stepwise regression. These types of regressions are used to predict, extract curvilinear data, and fit regression models with predictive models.
In a hospital setting that involves descriiption or prediction, data mining is used as a regression analysis tool. Data mining uses the methodology and technology to apply various amounts of data for decision-making. According to Koh & Tan (2011), “To illustrate a data mining application in healthcare, suppose that as part of its healthcare management program, HealthOrg (a fictional healthcare organization) is interested in finding out how certain variables are associated with the onset of diabetes. The purpose of this data mining application is to identify high-risk individuals so appropriate messages can be communicated to them. A dataset exists in the data warehouse of HealthOrg that contains the following seven variables of particular interest to HealthOrg: gender, age, body mass index (BMI), waist-hip ratio (WHR), smoking status, the number of times a patient exercises per week, and the onset of diabetes, which is the target variable, measured by a dichotomous variable indicating whether an individual has tested positive for diabetes. The dataset comprises 262, or 12.78 percent, of positive diabetic cases and 1,778, or 87.22 percent, of negative non-diabetic cases. After reviewing the work of Breault et al on the data mining of a diabetic data warehouse,28 HealthOrg decides that the decision tree is an appropriate data mining technique to use to find out how certain variables are associated with the onset of diabetes. (p.68)”