## 2 Materials and Methods
### Descriptive Statistics  
Descriptive statistics such as mean, standard deviation (StDev), mean standard error (SE Mean), minimum, maximum, the 1 st quartile, the 2 nd quartile, the 3 rd quartile, kurtosis, and skewness of variables containing quantitative data were calculated to provide information about the study data. Table 3 summarizes the descriptive statistics of the dependent and independent numerical variables. The mean for the t variable is calculated as 43.350, and the standard deviation is 75.60, indicating that the observation values are distributed in a very narrow range over time. The means of the _Wd_ and _Nd_ variables are 174.46\({}^{\circ}\) and 174.02\({}^{\circ}\), respectively, and the distributions of these variables are similar. However, a wider variance is observed in technical and environmental variables such as _Ws_ and _Rs_; significantly, the variance of _Ws_ is high at 75.368. As the dependent variable, _Po_ has an average value of 0.383 MW and varies with a standard deviation value of 0.478. Significant differences between the minimum and maximum values are striking when the details of the statistics are examined. While the minimum values of Ws and Rs are determined to be 0.0, the observations reaching the maximum are mainly seen in the 3 rd quarter values. This situation reflects the variability in wind farms' operating efficiency and conditions. At the same time, the low median value of _Po_ (0.210 MW) indicates that power production is highly dependent on the changing environmental and technical conditions. The table comprehensively overviews these variables' central tendency and distribution. Table 4 summarizes the relationships between variables using the Pearson correlation test results. Correlation coefficients indicate strong, weak, or negligible relationships between variables [27]. According to the table, the highest correlation was determined as \(r=0.964\) between _Nd_ and _Wd_. This strong positive correlation shows that the orientation of \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Variable** & **t** & **Wd** & **Ws** & **Nd** & **Rs** & **Po** \\ \hline Mean & 43,350 & 174.46 & 57,902 & 174.02 & 11,154 & 0.383 \\ SE Mean & 0.190 & 0.268 & 0.007 & 0.27 & 0.012 & 0.001 \\ StDev & 75.60 & 106.5 & 27,453 & 107.45 & 4629 & 0.478 \\ Variance & 5709 & 11,342.6 & 75,368 & 11,544.9 & 21,426 & 0.229 \\ Minimum & 43,221 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ The 1 st Quartile & 43,285 & 66.47 & 37,700 & 64.710 & 8270 & 0.060 \\ The 2 nd Quartile & 43,349 & 181.63 & 55,200 & 181.91 & 11,120 & 0.210 \\ The 3 rd Quartile & 43,414 & 267.46 & 74,000 & 267.83 & 14,540 & 0.500 \\ Maximum & 43,481 & 359.57 & 258,200 & 359.83 & 31,880 & 265,000 \\ Skewness & 0.040 & \(-\)0.040 & 0.840 & \(-\)0.04 & \(-\)0.310 & 2.100 \\ Kurtosis & \(-\)1.190 & \(-\)1.310 & 1.480 & \(-\)1.32 & \(-\)0.070 & 4.590 \\ \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics data for variables other than categorical variables. the turbine depends mainly on the _Wd_. The correlation between _Po_ and _Ws_ is also relatively high at \(r=0.896\), which reveals that _Ws_ is a significant determinant in power generation. In addition, the correlation between _Rs_ and _Ws_ is \(r=0.842\), which shows that the operating speed of the turbines is directly dependent on the wind speed. Weaker or negligible correlations are seen among other variables. The correlation between t and Po is limited to \(r=0.274\), which indicates that power generation varies little over time. When looking at the negative correlations, a low value such as \(r=-0.046\) between Rs and Wd suggests no significant relationship between the two variables. The table also emphasizes that all \(p\)-values are 0 or minimal, and the results are statistically significant. However, the negligible correlation between some variables indicates that they can be considered independently or their effects on the system are limited.