## 7 Conclusions and Suggestions  
The power transformation approach is used in this research to generalize the Zeghdoudi distribution of one parameter to a power Zeghdoudi distribution of two parameters. Some statistical properties, including moments, reliability functions, order statistics, Bonferroni and Lorenz curves, Gini index, stochastic ordering, quantile function, and mean and median deviations are derived. Numerous estimation techniques are considered, such as the maximum likelihood estimation, least squares, weighted least squares, maximum product of spacing methods, the [PERSON] method, and the right-tail and left-tail Anderson-Darling estimation methods. A simulation study is carried out to assess these estimation approaches and determine the distribution properties. Three actual data sets are examined and fitted and the Anderson-Darling and Nikulin-Rao-Robson test statistics are considered for the novel PZD to investigate the distribution's applicability in different fields compared with the gamma, xgamma, Lomax, Darna, power Darna, power Lindley, and exponentiated power Lindley models. It is found that PZD often performs better than other distributions in fitting the three data sets considered in this study. In addition, the MSE values of different proposed estimators decrease for increased values of the sample size. Future work could include bivariate extensions, transmutation extensions, discrete extensions, and additional regression models. In addition, the PZD parameters can be estimated using the ranked set sampling method and some of its modifications. See [47] for stratified median ranked set sampling and [48] for double percentile ranked set samples. Conceptualization, A.I.A.-O. ; Methodology, K.A. and A.I.A.-O. ; Software, K.A. and A.I.A.-O. ; Formal analysis, K.A. ; Investigation, K.A., A.I.A.-O. and R.A.; Data curation, K.A. ; Writing--original draft, K.A., A.I.A.-O. and R.A.; Writing--review & editing, A.I.A.-O. and R.A.; Funding acquisition, R.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data used in this study are available within the paper. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Distributions** & \multicolumn{3}{c}{**Estimances**} \\ \hline
**PZD** & \(\hat{\beta}=0.3152,\hat{\theta}=3.5162\) & \\
**ZD** & \(\hat{\theta}=3.5614\) & \\
**Lomax** & \(\hat{\alpha}=2.1635,\ \hat{\lambda}=0.7263\) & \\
**Xgamma** & \(\hat{\theta}=1.5312\) & \\
**Gamma** & \(\hat{\alpha}=3.1259,\ \hat{\beta}=0.0985\) & \\
**Darna** & \(\hat{\alpha}=0.8132,\ \hat{\beta}=1.7452\) & \\
**Power Lindley** & \(\hat{\alpha}=1.3416\) & \(\hat{\beta}=1.9236\) & \\
**Power Darna** & \(\hat{\alpha}=1.285,\ \hat{\beta}=2.6351,\ \hat{\theta}=0.0632\) & \\
**EP LD** & \(\hat{\alpha}=6.2451,\ \hat{\beta}=0.7325,\ \hat{\theta}=4.201\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: ML parameter estimates for the alternative distributions with glass fiber data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Distributions** & \(\mathbf{Y^{2}}\) & **A** & **W** & **K-S (\(p\)-Value)** & **-NLL** & **AIC** & **BIC** \\ \hline
**PZD** & 7.2365 & 0.5623 & 0.0785 & 0.0422 (0.8496) & 89.623 & 183.246 & 187.532 \\
**ZD** & 8.6235 & 0.6748 & 0.0946 & 0.0512 (0.8348) & 93.516 & 189.032 & 191.175 \\
**Darma** & 9.1532 & 0.7146 & 0.1016 & 0.0533 (0.8273) & 94.623 & 193.246 & 197.532 \\
**Xgamma** & 12.130 & 0.8189 & 0.1203 & 0.0862 (0.7869) & 99.889 & 201.778 & 203.921 \\
**Gamma** & 11.846 & 0.8133 & 0.1176 & 0.0785 (0.7902) & 98.495 & 200.99 & 205.276 \\
**Lomax** & 11.526 & 0.8041 & 0.1135 & 0.0674 (0.7945) & 97.648 & 199.296 & 203.582 \\
**Power Lindley** & 9.8462 & 0.7688 & 0.1078 & 0.0584 (0.8135) & 95.784 & 195.568 & 199.854 \\
**Power Dama** & 7.5263 & 0.6325 & 0.0823 & 0.0476 (0.8412) & 91.253 & 188.506 & 194.935 \\
**EP LD** & 10.236 & 0.7914 & 0.1106 & 0.0624 (0.8053) & 96.667 & 199.334 & 205.763 \\ \hline \hline \end{tabular}
\end{table}