MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making Name Capella university MHA-FPX 5017 Data Analysis for Health Care Decisions Presenting Statistical Results for Decision Making

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). Regression Method 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). MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making Regression Statistics As illustrated in Fig. 1, several statistics are employed to evaluate the fit of a regression model, indicating how well it aligns with the data. Multiple R The correlation coefficient, multiple R, measures the strength of the linear relationship between the predictor variable and the response variable. A multiple R of 1 signifies a perfect linear relationship, while a multiple R of 0 suggests no linear relationship whatsoever (Kraus et al., 2021). R Squared The coefficient of determination, also known as r2, signifies the variance explained by a predictor variable, representing the proportion of variance in the response variable. An r2 of 1 indicates that the regression predictions perfectly match the data. The r2 value of 11.3% implies that the response variable can be entirely explained by the predictor variable (Kraus et al., 2021; Shipe et al., 2019). ANOVA In Figure 2, ANOVA, the F statistic p-value, located at the bottom of the table, is crucial for determining the overall significance of the regression model. If the p-value is less than the significance level (usually .05), there is sufficient evidence to conclude that the regression model fits the data better than the model without predictor variables. Thus, the predictor variables enhance the model’s fit (Kraus et al., 2021; Shipe et al., 2019). In Figure 3, coefficient estimates, standard errors, p-values, and confidence intervals for each term in the regression model are presented. Each term receives a coefficient estimate, standard error estimate, t-statistic, p-value, and confidence interval (Shipe et al., 2019). Conclusion According to the multiple regression results, the variables considered account for 11.31% of the variance, indicating that changing costs would cause an 11.31% increase. Healthcare professionals continually seek ways to reduce costs while maintaining high-quality care for their patients. The model’s significant impacts, below 0.05, warrant consideration in decision-making (Shipe et al., 2019). References Davenport, T. H. (2014). A Predictive Analytics Primer. Harvard Business Review Digital Articles, 2–4. https://web-s-ebscohostcom.library.capella.edu/ehost/pdfviewer/pdfviewer?vid=2&sid=3d6a776e-ccaa-4746-a332-24bafb60e468%40redis Kraus, D., Oettinger, F., Kiefer, J., Bannasch, H., Stark, G. B., & Simunovic, F. (2021). Efficacy and Cost-Benefit Analysis of Magnetic Resonance Imaging in the Follow-Up of Soft Tissue Sarcomas of the Extremities and Trunk. Journal of Oncology, 2021. https://doi.org/10.1155/2021/5580431 MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making Palmer, P. B., & O’Connell, D. G. (2009). Regression analysis for prediction: Understanding the process. Cardiopulmonary Physical Therapy Journal, 20(3), 23–26. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845248/ Shipe, M. E., Deppen, S. A., Farjah, F., & Grogan, E. L. (2019). Developing prediction models for clinical use using logistic regression: An overview. Journal of Thoracic Disease, 11(S4), S579–S584. https://doi.org/10.21037/jtd.2019.01.25