Introduction
A coherent and efficient presentation of evidence-based data collection is crucial when communicating with healthcare administrators. Healthcare researchers employ multiple regression analyses to evaluate the strength of the relationship between a dependent variable and several predictor variables. Given the dynamic nature of healthcare, comprehending and presenting data is imperative for identifying trends, whether positive or negative. Regression analysis is an effective statistical method for analyzing medical data, enabling the identification and characterization of relationships among multiple factors. However, if decision-makers fail to grasp the results of data analysis, its utility is compromised. The process of data analysis commences with understanding the problem, goals, and intended actions. Consequently, the analysis yields evidence to either support or refute the hypothesized idea (Davenport, 2014).
The multiple regression equation is represented as y = a + b1x1 + b2x2 + … + bkxk, where x1, x2, …, xk denote the k independent variables (e.g., age, risk, satisfaction), and y (cost) represents the dependent variable. Multiple regression analysis allows for the explicit control of numerous other factors influencing the dependent variable simultaneously. Through regression analysis, one or more independent variables are compared to a dependent variable, and based on a linear combination of predictors, a predicted value is computed for the criterion. Regression analysis serves two primary purposes in science: prediction, including classification, and explanation (Palmer & O’Connell, 2009).
Struggling with online classes or exams? Get expert help to ace your coursework, assignments, and tests stress-free!