MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making MHA-FPX 5017 Data Analysis for Health Care Decisions Presenting Statistical Results for Decision Making

Introduction

A clear and effective presentation of evidence-based data collection is essential when communicating with healthcare administrators. Healthcare researchers utilize multiple regression analyses to assess the strength of the relationship between a dependent variable and several predictor variables. Given the dynamic nature of healthcare, understanding and presenting data is crucial for identifying trends, whether they are positive or negative. Regression analysis serves as an effective statistical method for analyzing medical data, allowing for the identification and characterization of relationships among multiple factors. However, if decision-makers do not comprehend the results of data analysis, its utility is diminished. The data analysis process begins with understanding the problem, goals, and intended actions, ultimately yielding evidence to support or refute the hypothesized idea (Davenport, 2014).

Regression Method

The multiple regression equation is expressed as y = a + b1x1 + b2x2 + … + bkxk, where x1, x2, …, xk represent the k independent variables (e.g., age, risk, satisfaction), and y (cost) denotes the dependent variable. Multiple regression analysis allows for the simultaneous control of various factors influencing the dependent variable. Through regression analysis, one or more independent variables are compared to a dependent variable, and a predicted value is computed 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).

Regression Statistics

As illustrated in Figure 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 indicates a perfect linear relationship, while a multiple R of 0 suggests no linear relationship at all (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. An R² value of 11.3% implies that the response variable can be 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 (typically .05), there is sufficient evidence to conclude that the regression model fits the data better than a 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 changes in costs would result in an 11.31% increase. Healthcare professionals are continually seeking ways to reduce costs while maintaining high-quality care for their patients. The model’s significant impacts, with p-values 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