DNP-MODULE 7 Y 8

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Module 7 Biostatistics Assignment 1

Table 1: Excel Regression Output

Analysis and Conclusion

The table above shows the regression output of Excel that was conducted between V2

and V2, in which V2 is the independent variable while V1 is the dependent Variable in the

model. The regression analysis found that the bivariate nexus between the two variables is

positive and significant. This is because the coefficient of V2 in the model is 0.220549.

Additionally, the value of R-squared in the model is 0.879739. This means that the 87.97 percent

change in the dependent variable V1 is accounted for and can be explained by the variation of the

Independent variable V2 (Chicco et al., 2021). Secondly, using the one-way ANOVA analysis,

the analysis showed that the value of F is 51.206 with the significance level of 0.00018; hence,

this is regarded as statistically significant. Therefore, the values of the V2 score highly correlate

positively with the V1 scores.

V1 = 3.148 + 0.221*V2

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It is possible to draw a few inferences by extracting the above regression results in Excel.

The high value of R-squared of 87.9% signifies that the model is approximated to have the

power of explaining the variation in the dependent variable V1 as the percentage of the value is

assumed to be moderately high. The small P value of less than 0.05 supports and illustrates the

fact that the relationship is statistically significant (Ali & Younas, 2021). The correlation of the

two variables is great, though the sample is small. The significant value of the independent

variable, 0.220549, is also useful in the sense that it indicates the fact that there is a positive

relationship between the independent variable and the other variables, given the rest being

constant, and, in the process, V1 will increase by 0.220549 when V2 also increases by a unit.

Lastly, it can be used in clinical experiments to furnish the information required in decision-

making to a considerable degree.

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References

Ali, P., & Younas, A. (2021). Understanding and Interpreting Regression Analysis. Evidence-

Based Nursing, 24(4), 116–118. https://doi.org/10.1136/ebnurs-2021-103425

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The Coefficient of Determination R-squared Is

More Informative than SMAPE, MAE, MAPE, MSE, and RMSE in Regression Analysis

Evaluation. PeerJ Computer Science, 7(5), e623. ncbi. https://doi.org/10.7717/peerj-

cs.623