## 8 Summary and Conclusions  
The unit-exponentiated half-logistic distribution, which is useful for modelling data on the unit interval, was proposed in this study as a result of an investigation into a suitable transformation. The mathematical properties of this distribution, such as moments, probability-weighted moments, incomplete moments, different entropy measures, and stress-strength reliability, were provided. The parameter estimators of the suggested distribution were established using the maximum likelihood, maximum product of spacing, and Bayesian methods. For the purpose of evaluating how well parameter estimates performed on finite samples, a thorough simulation study was included. The effectiveness of parameter estimation methodologies on finite samples was examined through a comprehensive simulation examination. We assessed the performance of point estimates in terms of their bias and MSE, while the interval estimates were investigated in terms of their length. We concluded that, in most cases, the smaller accuracy measures of the MPS estimates made them preferable to the ML estimates. The MSE reduced for each estimate as the sample size grew, demonstrating the consistency of the estimates. The length of the CI estimates based on the three techniques reduced as the sample size increased, indicating that the CI was the shortest. For clarification, the suggested distribution was practically applied to data on economic growth and tensile strength. Additionally, COVID-19 data analysis using British medical statistical data was supplied. In comparison to several new unit distributions and existing unbounded distributions, the experimental data showed that the proposed UEHLD distribution delivered a better outcome. It is important to note that, when analyzing the COVID-19 data, the result of our novel model produced superior measure values than the models shown by [PERSON] et al. (2017); [PERSON] et al. (2018); [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] et al. (2020); [PERSON] et al. (2020). Additionally, estimates of the new model were taken into account for each dataset using various estimation techniques, with the exception of the first dataset, for which the MPS approach was not used due to its more equally distributed values. The UEHLD's log-likelihood was shown graphically together with the representations of the contour plots with varied parameter values. For the three datasets, histograms of the marginal posterior density estimates were provided along with plots of the posterior distributions of the parameters.  
Conceptualization, A.S.H., A.F. and A.A.; methodology, A.S.H., A.F. and E.M.A.; software, A.S.H. and E.M.A.; validation, A.S.H., A.F., A.A. and E.M.A.; formal analysis, A.S.H. and E.M.A.; investigation, A.F. and A.A.; resources, A.S.H. and A.A.; data curation, A.F. and E.M.A.; writing--original draft preparation, A.S.H., A.F. and E.M.A.; writing--review and editing, A.S.H., A.F., A.A. and E.M.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data sets are available in this paper. The authors declare no conflict of interest.