# Statistical Methods in Bidding Decision Support for Construction Companies
## 3 Results and Discussion
### LOG Model--The Model Using Regression Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **Wilks’ Lambda** & **Partial Lambda** & **The Value of the \(F\)** & \multirow{2}{*}{**Topance**} & **Statistically** \\  & **Lambda** & **Wilks** & **Statistic** & & & \\ \hline x\({}_{1}\) & 0.430141 & 0.961162 & 2.90937 & 0.092377 & 0.377706 & 0.62294 & \\ x\({}_{2}\) & 0.413447 & 0.999971 & 0.00207 & 0.963850 & 0.370875 & 0.629125 & \\ x\({}_{3}\) & 0.449346 & 0.920082 & 6.25390 & 0.014669 & 0.376703 & 0.623297 & * \\ x\({}_{4}\) & 0.421275 & 0.981391 & 1.36523 & 0.246487 & 0.522505 & 0.477495 & \\ x\({}_{5}\) & 0.454559 & 0.909531 & 7.16170 & 0.009215 & 0.109267 & 0.890733 & * \\ x\({}_{6}\) & 0.609746 & 0.678045 & 34.18763 & 0.000000 & 0.643071 & 0.356929 & * \\ x\({}_{7}\) & 0.419668 & 0.985148 & 1.08548 & 0.300962 & 0.906009 & 0.903991 & \\ x\({}_{8}\) & 0.434939 & 0.950588 & 3.74495 & 0.56895 & 0.577896 & 0.422104 & \\ x\({}_{9}\) & 0.414878 & 0.996522 & 0.25127 & 0.617712 & 0.757040 & 0.242960 & \\ x\({}_{10}\) & 0.417598 & 0.990032 & 0.72491 & 0.397361 & 0.672704 & 0.327296 & \\ x\({}_{11}\) & 0.424018 & 0.975042 & 1.84297 & 0.178843 & 0.633470 & 0.366531 & \\ x\({}_{12}\) & 0.430727 & 0.959854 & 3.01140 & 0.086960 & 0.512083 & 0.487917 & \\ x\({}_{13}\) & 0.444633 & 0.929835 & 5.43308 & 0.022563 & 0.513878 & 0.486123 & \\ x\({}_{14}\) & 0.414026 & 0.998573 & 0.10292 & 0.749283 & 0.580513 & 0.419487 & \\ x\({}_{15}\) & 0.434007 & 0.952600 & 3.58258 & 0.062408 & 0.391804 & 0.608196 & \\ \hline \hline \end{tabular}  
* Significance level \(\alpha=0.05\).  
\end{table} 
Table 6: Evaluation of the parameters of the discriminant function—15 explanatory variables. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **Wilks’ Lambda** & **Partial Lambda Wilks** & **The Value of the F Statistic** & \(p\)**Value** & **Tolerance** & **Wilks’ Lambda** \\ \hline x\({}_{1}\) & 0.570048 & 0.825309 & 17.14504 & 0.000084 & 0.694765 & * \\ x\({}_{3}\) & 0.495679 & 0.949133 & 4.341000 & 0.040358 & 0.771227 & * \\ x\({}_{5}\) & 0.557289 & 0.844204 & 14.94840 & 0.000222 & 0.541454 & * \\ x\({}_{6}\) & 0.740919 & 0.634977 & 46.56377 & 0.000000 & 0.776650 & * \\ x\({}_{13}\) & 0.532386 & 0.883694 & 10.66070 & 0.001605 & 0.803942 & * \\ x\({}_{15}\) & 0.500913 & 0.939217 & 5.242020 & 0.024650 & 0.789131 & * \\ \hline \hline \end{tabular}  
* Significance level \(\alpha=0.05\).  
\end{table} 
Table 7: Evaluation of the discriminant function parameters—final model (six input variables). The next step of the analysis is to check the statistical significance of the discriminant function (Table 8) and to determine its coefficients. The eigenvalue of a discriminant function represents the ratio of the between-group variance to the within-group variance. Large eigenvalues characterize functions with high discriminatory power. Canonical correlation is a measure of the magnitude of the association between a grouping variable and the results of a discriminant function. It ranges from \(<\)0, 1>, where 0 means no relationship and 1 means maximum relationship. The value of 0.727691 means that the function is related to a grouping variable. The value of Wilks' Lambda is acceptable. The value of \(p\) = 0.000000 \(<\) 0.05. The proposed discriminant function is statistically significant and ultimately takes the following form:  
\[\text{D}=-12.831+0.509x_{1}+0.437x_{3}-0.464x_{5}+1.502x_{6}+0.615x_{13}-0.429 x_{15} \tag{14}\]  
The next stage of the analysis is the classification procedure using classification functions.