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

Regression Models in Modern Decision Making

The importance of statistics in today’s decision-making processes gives directors less confidence when they have to deal with doubts about the huge amount of data that is available.  This confidence helps directors form well-informed opinions and lead their teams in a steady way, which makes the organization more effective.  Colorful regression models have attracted the interest of contemporary scholars because they can combine information, create useful variables, build accurate models, and analyze how well these models fit the data that has been collected (Casson & Farmer, 2014).  This analysis seeks to predict the required payment amount for the subsequent period based on a dataset that includes hospital costs, patient durations, risk factors, and satisfaction ratings from the previous period.

Significance Testing and Effect Size of Regression Coefficients

Statistical methods are very important for making decisions in organizations.  It is essential to utilize various regression analysis methodologies to formulate an equation that accurately reflects the statistical relationship between a response variable and one or more predictor variables (SCSUEcon, 2011).  The p-value is important for figuring out the effect size of the measure in a regression equation because it lets you test the null hypothesis. 

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

Regression Modeling for Predictive Analysis

When trying to figure out how much to pay, a retrogression model that uses age, threat, and satisfaction datasets shows an explicatory friction of 11 (Gaalan et al., 2019).  It’s important to remember that not all independent variables make this friction worse; instead, you need to look at each variable’s percentile contribution to see how well the model fits.  The multiple regression model shows statistical significance, with F(3,181) = 7.69, P < .001, and R² = .11.

Statistical Results and Decision Making

Statistical Outcomes and Determination  Using the data from the handed dataset, multiple regression equations can help healthcare professionals make decisions about predicted payment costs for each case.  You can figure out how much each case will cost by using the equation y = 6652.176 + 107.036(age) + 153.557(threat) – 9.195*(satisfaction).  Below are examples of predicted payment costs for certain cases from rows 13, 20, and 4.

Conclusion

To optimize healthcare payment costs, it may be wise to include the satisfaction variable from prophetic models, as it seems to be inconsistent with other predictor variables.  Still, using colorful retrogression models is still important for forming opinions and staying true to the long-term goals of the organization.  Even though there are some adaptations that don’t involve supervision, healthcare associations can use retrogression analysis to deal with doubts and plan for future payment costs in a smart way.

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

Schneider, A., Hommel, G., & Blettner, M. (2010).  Linear regression analysis is the 14th part of a series on how to evaluate scientific papers.  Deutsches Arzteblatt transnational, 107(44), 776–782.

 SCSUEcon. (2011).  Linear regression in Excel (Video). Restate. Taken back from YouTube.com.

 Sullivan, G. M., & Feinn, R. (2012).  Using effect size—why the P-value is insufficient.  Journal of Graduate Medical Education, 4(3), 279–282.