MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making

Student Name

Capella University

MHA-FPX 5017 Data Analysis for Health Care Decisions

Prof. Name

Date

Presenting Statistical Results for Decision Making

Introduction

A well-structured and effective presentation of evidence-based data is crucial for effective communication with healthcare administrators. Within healthcare research, multiple regression analyses play a vital role in assessing the relationship between a dependent variable and several predictor variables. Understanding and presenting data is essential in identifying trends within the dynamic healthcare landscape, whether positive or negative. Regression analysis is a robust statistical method for analyzing medical data, enabling the identification and characterization of relationships among multiple factors. However, the utility of data analysis diminishes if decision-makers struggle to comprehend the results. The data analysis process begins with a clear understanding of the problem, goals, and intended actions, with the analysis providing evidence either to support or refute the hypothesized idea (Davenport, 2014).

Regression Method

The multiple regression equation, represented as ( y = a + b_1x_1 + b_2x_2 + … + b_kx_k ), where ( x_1, x_2, …, x_k ) represent the independent variables (e.g., age, risk, satisfaction), and ( y ) (cost) represents the dependent variable. Multiple regression analysis allows for the explicit control of numerous factors influencing the dependent variable simultaneously. This method compares one or more independent variables to a dependent variable, computing a predicted value for the criterion based on a linear combination of predictors. 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 depicted in Table 1, several statistics are utilized to assess the fit of a regression model, indicating its alignment with the data.

Table 1: Regression Statistics

Multiple R

The correlation coefficient, multiple R, quantifies the strength of the linear relationship between the predictor variable and the response variable. A multiple R of 1 indicates a perfect linear relationship, while a multiple R of 0 suggests no linear relationship (Kraus et al., 2021).

R Squared

The coefficient of determination, or ( r^2 ), indicates the proportion of variance in the response variable explained by the predictor variable. An ( r^2 ) of 1 suggests perfect alignment between regression predictions and data. An ( r^2 ) value of 11.3% implies that the response variable’s variance can be entirely explained by the predictor variable (Kraus et al., 2021; Shipe et al., 2019).

ANOVA

ANOVA, as shown in Table 2, utilizes the F statistic p-value to determine the overall significance of the regression model. If the p-value is less than the significance level (usually .05), it indicates that the regression model fits the data better than the model without predictor variables, thus enhancing the model’s fit (Kraus et al., 2021; Shipe et al., 2019).

Conclusion

According to the multiple regression results, the variables considered account for 11.31% of the variance, indicating that altering costs would result in an 11.31% increase. Healthcare professionals continuously seek methods 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. [Link]

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. [DOI Link]

Palmer, P. B., & O’Connell, D. G. (2009). Regression analysis for prediction: Understanding the process. Cardiopulmonary Physical Therapy Journal, 20(3), 23–26. [Link]

MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making

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. [DOI Link]