## 4 Methods
### Statistical Analysis of Defect Types  
Taking into account reports appearing during the warranty period, the defects were divided into several groups. The first three groups included reports concerning problems with electrical, plumbing, and central heating (c.h.) installations in apartments. The next groups are reports concerning window and door joinery. Quite often, there were defects consisting of dampness, mainly of walls, from different causes. Separately, reports of wall scratches and other problems related to elements of apartment finishing (such as floors) were grouped. The last group is various defects occurring in the area of balconies and terraces. For each of the buildings, the number of defects was determined in the range of 1-3, 4-6, 7-12, and 13-26 months from the date of acceptance. The list is presented in Table 9.  
The most numerous group is woodwork defects, and the least numerous group is central heating system faults. These dependencies are the same in all analyzed buildings. In order to confirm that the number of defects appearing in individual groups is not accidental, the correlation between the numbers of defects in Building A and Building B in the first three months after acceptance was checked (Table 10). Building C was not taken into account due to insufficient data. Then, a correlation was established between all defects reported in all three buildings over the entire observation period (Table 11). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Building**} & \multirow{2}{*}{**Number of Defects**} & \multicolumn{2}{c}{**After 3 mth**} & \multicolumn{2}{c}{**After 12 mth**} \\ \cline{3-5}  & & **No.** & **\%** & **No.** & **\%** \\ \hline A & 40 & 2 & 5.00 & 24 & 60.00 \\ B & 25 & 1 & 4.00 & 10 & 40.00 \\ C & 22 & 5 & 22.73 & 16 & 72.73 \\ Total & 87 & 8 & 9.20 & 50 & 57.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Defects reported by the manager concerning the common parts. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Building**} & \multirow{2}{*}{**Number of Defects**} & \multicolumn{2}{c}{**After 3 mth**} & \multicolumn{2}{c}{**After 12 mth**} \\ \cline{3-10}  & & **No.** & **\%** & **No.** & **\%** \\ \hline A & 204 & 54 & 26.47 & 120 & 58.82 \\ B & 213 & 40 & 18.78 & 121 & 56.81 \\ C & 41 & 7 & 17.07 & 29 & 70.73 \\ Total & 458 & 101 & 22.05 & 270 & 58.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Defects reported by the owners of the flat concerning the residential part of the building. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Building**} & \multirow{2}{*}{**Time (mth)**} & **Electr. ** & **Plumb. ** & **c.h. ** & **c.h. ** & **Joinery** & **Dampness** & **Scratches** & **Terraces** & **Other** \\ \hline A & 1-3 & 26 & 2 & 9 & 12 & 3 & 4 & 5 & 0 \\  & 4-6 & 3 & 0 & 0 & 8 & 0 & 7 & 0 & 1 \\  & 7-12 & 5 & 16 & 5 & 21 & 9 & 3 & 12 & 2 \\  & 13-26 & 11 & 7 & 3 & 25 & 8 & 21 & 19 & 3 \\ Total  
The analyses performed allow us to confirm the hypothesis about the lack of randomness of the collected data. Correlation for data from Table 10 allows us to confirm a very strong correlation, while the significance level allows us to reject the hypothesis of zero correlation. For the data in Table 11, the indicators show a very strong correlation between the data for Buildings A and B and a strong correlation for Building C.  
For the data concerning all defects (Table 12), the correlation coefficients and the coefficients of determination for individual defect groups were checked. This was to find the relationship between the analyzed groups. Figures 3-5 show the graphs of the number of defects in individual time intervals, respectively, for defects that appear in a similar distribution over time.