MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making Regression Statistics

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