## 3 Results  
In the context of the examination of linear dependency analysis, all numerical variables characterizing inputs (outdoor temperature, depth) and characterizing outputs (CH4, CO2, TP, PID, O2) were observed. We analysed all indicators in pair, whereas we detected statistically significant positive and negative correlations. The results of the analysis draw attention to two positive and two negative correlations, according to Table 2.  
Positive linear dependence, defined by direct proportion, was confirmed between the indicators:  
* TP and CH4, which has a very strong dependency with a correlation coefficient \(r\) = 0.81 if high TP values have been measured, then the high CH4 values would be also found. * TP and PID have mild dependency level with a correlation coefficient \(r\) = 0.49, this dependence define mutual growth, or decrease of monitored variables. Both dependencies were assessed by the software as statistically significant. Negative linear dependence, defined by inverse proportion, was confirmed among the indicators:
* for O2 and CO2 we are dealing with a medium strong dependence with a correlation coefficient \(r\) = \(-\)0.6158;
* for vapor temperature and borehole SMV N (specification of borehole (SMV), number N) there is a medium level of dependence with a correlation coefficient \(r\) = \(-\)0.43, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **by Variable** & **Correlation** & **Signif Prob** & \multicolumn{2}{c}{**Positive and Negative Correlation Coefficients**} \\ \hline PID (mg/m3) & CH4 (mg/m3) & 0.1076 & 0.1438 & & & \\ Vapor temp. (\({}^{\circ}\)C) & CO2 (mg/m3) & 0.1126 & 0.1259 & & & \\ Depth (m) & CH4 (mg/m3) & 0.1268 & 0.0845 & & & \\ depth (m) & TP (mg/m3) & 0.1533 & 0.0368 * & & & \\ Depth (m) & CO2 (mg/m3) & \(-\)0.1558 & 0.0337 * & & & \\ Vapor temp. (\({}^{\circ}\)C) & O2 (\%) & \(-\)0.1567 & 0.0327 * & & & \\ O2 (\%) & well SMV N. & 0.1643 & 0.0250 * & & & \\ PID (mg/m3) & well SMV N. & \(-\)0.1824 & 0.0127 * & & & \\ Vapor temp. (\({}^{\circ}\)C) & well SMV N. & \(-\)0.4337 & \(<\)0001 * & & & \\ PID (mg/m3) & TP (mg/m3) & 0.4942 & \(<\)0001 * & & & \\ O2 (\%) & CO2 (mg/m3) & \(-\)0.6158 & \(<\)0001 * & & & \\ TP (mg/m3) & CH4 (mg/m3) & 0.8127 & \(<\)0001 * & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Output of paired correlation indicators. The results of the regression analysis are shown in Figures 2 and 3. The mathematical expression of both dependences is statistically significant and describes an important part of the unknown. In the case of TP + CH\({}_{4}\) has to do with linear dependence, when the mathematical equation describes 66% unknown. Relationship between O\({}_{2}\) and CO\({}_{2}\) is defined by a second order polynomial and describes 73% of the unknown. Following the results of the variation coefficient in descriptive statistics presented above, it was necessary to examine the reasons for such high variability of the four variables measured--CH\({}_{4}\), CO\({}_{2}\), TP, PID. As already indicated above, two variables entered the measurement, which could have influenced this--the depth of measurement and the date. The analysis was carried out by a non-parametric Wilcox test. The results of the Wilcox test showed that this was not a statistically significant variability of the measured values for the parameters CH\({}_{4}\), TP and PID due to the depth of measurement. Wilcox test results showed that this was a statistically significant variability of measured values for CO\({}_{2}\) parameters according to depth and date and PID by date, visible at Figures 4-6 and Tables 5-7. Figure 5: Analysis of variability CO\({}_{2}\) with the influence of the depth. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ChiSquare** & **DF** & **Prob \textgreater{} ChiSq** \\ \hline
47.2986 & 12 & \textless{}0.0001 \\ \hline DF: Records the associated degrees of freedom (DF for short) for each source of variation.  
\end{table}