Regression Models in Modern Decision Making 

MHA FPX 5017 Assessment 3 The importance of data in modern decision -making strengthens leaders with more confidence to navigate uncertainties in the middle of the abundance of available data. This trust enables managers to make informed decisions and provide stable management to their employees, thus strengthening organizational efficiency. Various regression models have attracted attention from modern scholars because of the ability to synthesize information from modern scholars, create meaningful variables, create real models and accommodate the collected data (Kaisan & Farmer, 2014). The purpose of this analysis is to predict the necessary refund amount from the previous year to the cost of a dataset, the patient’s age, the risk factor and a dataset, including satisfaction points from the previous year.

Significance Testing and Effect Size of Regression Coefficients

Statistical function plays an important role in organizational decision -making processes. In order to install a varied regression analysis methods to establish an equation that effectively captures the statistical correlation between a response variable and one or more prophet variables, it is compulsory (SCSUECON, 2011). The P-Mind assesses the importance of determining the size of the coefficient in a regression equation, as it enables testing of disabled hypothesis. A low p-value (<0.05) reflects rejection of disabled hypothesis, indicating significant progress in several regression models and changes seen in the response variables related to variation in prophet values (Sulivan and Fin, 2012).

 MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models  

Regression Modeling for Predictive Analysis

By predicting the reimbursement amount, a regression model that includes age, risk and satisfaction data set reveals an explanatory variance of 11% (Galan et al., 2019). It is important to note that not all independent variables contribute equally to this variance; Rather, the percentage contribution to each variable should be considered to understand the model’s suitability. Several regulatory models show statistical significance, with f (3,181) = 7.69, p <0.001 and R2 = .11.

Statistical Results and Decision Making 

MHA FPX 5017 Assessment 3 By using data from the stated dataset, many regression equations can support health decisions on the estimated reimbursement costs for individual patients. The reimbursement costs for each patient can be calculated using the equation: Y = 6652.176 + 107,036 (age) + 153,557 (risk) – 9.195*(satisfaction). Examples of estimated reimbursement costs for specific patients from rows 13, 20 and 44 are presented below.

Conclusion 

In order to optimize the cost of reimbursement of health services, it may be justifiable to exclude satisfaction variables from the future model, as it appears to be in violation of other prophecy stabs. However, the use of different regression models is necessary to make informed decisions and match long -term organizational goals. Despite potential regulatory adjustment, health organizations may benefit from regression analysis to navigate uncertainty and plan effectively for future reimbursement costs.

Reference 

Kaisan, R. J., and Farmer, L. D. M. (2014). Understand and examine the belief in linear regression: a primer for medical researchers. Clinical and Experimental Ophthalmology, 42 (6), 590–596. Galan, K., Kunavikul, W., Akkadacanant, T., Vichikham, O. A., and Tureel, S. (2019). Factors that predict the quality of nursing among nurses in hospitals in tertiary care in Mongolia. International Nursing Review, 72 (5), 53–68. Introtis Byu. (2016). Create a many linear regression in Excel . Transcript. Took from YouTube.com. https://www.amcp.org