MHA FPX 5017 Assessment 3: Predicting an Outcome Using Regression Models  

Regression Models in Modern Decision Making 

The importance of information in today’s decision-making gives leaders more confidence to deal with uncertainty in a world full of information.  This kind of trust gives managers the power to make good decisions and provide stable management for their employees, which makes the organization more efficient.  Several regression models have attracted the attention of modern scholars because they can combine data from modern scholars, create useful variables, create real models, and fit the data that has been collected (Kaisan & Farmer, 2014).  The aim of this analysis is to predict the necessary refund amount for the previous year, taking into account the dataset price, patient age, risk factor, and satisfaction points from the prior year.

Significance Testing and Effect Size of Regression Coefficients

Statistical function plays a significant role in organizational decision-making processes.

 It is necessary to use a diverse regression analysis technique to find an equation that accurately shows the statistical relationship between a response variable and one or more predictor variables (SCSUECON, 2011).  The P-Mind assesses the importance of determining the coefficient size in a regression equation, as it facilitates the testing of null hypotheses.  A low p-value (<0.05) signifies the rejection of the null hypothesis, demonstrating significant enhancements in various regression models and changes in response variables associated with fluctuations in prophet values (Sullivan and Fin, 2012).

MHA FPX 5017 Assessment 3: Predicting an Outcome Using Regression Models  

Regression Modeling for Predictive Analysis

A regression model utilizing age, risk, and satisfaction data sets to estimate the reimbursement value demonstrates an explanatory variance of 11% (Galan et al., 2019).  It is important to note that not all independent variables contribute equally to this variance. Instead, the percentage contribution of each variable must be analyzed to assess the model’s suitability.  Several regulatory models demonstrate statistical significance, with f (3,181) = 7.69, p < 0.001, and R² = .11. 

Statistical Results and Decision Making 

 Using data from the dataset, many regression equations can help make health decisions about how much each patient is likely to be reimbursed.  You can figure out how much a patient will be reimbursed by using the equation Y = 6652.176 + 107,036 (age) + 153,557 (risk) – 9.195*(satisfaction).  Here are some examples of estimated reimbursement costs for individual patients from rows 13, 20, and 44.

Conclusion 

 To optimize the reimbursement cost of healthcare services, it may be prudent to exclude satisfaction variables from the forthcoming model, as they appear to be inconsistent with other predictive indicators.  Nonetheless, the utilization of diverse regression models is essential to guarantee informed decision-making and the alignment of long-term organizational goals.  Healthcare organizations could use regression analysis to deal with uncertainty and plan for future reimbursement costs, no matter what changes are made to the rules.