# Financial Data Quality Evaluation Method Based on Multiple Linear Regression
## 4 Results
### Quality Evaluation Indicator System  
By following the AHP procedure and the selected primary and secondary indicators in Figure 2, the decision makers have to indicate preferences or priority for each decision alternative in terms of how it contributes to each criterion as shown in Table 5.  
Then, the following can be performed manually or automatically by the AHP software Expert Choice:  
1. Synthesizing the pair-wise comparison matrix (example: Table 5). 2. Calculating the priority vector for a criterion such as experience (example: Table 5). 3. Calculating the consistency ratio. 4. Calculating \(\lambda_{max}\). 5. Calculating the consistency index, \(CI\). 6. Selecting appropriate value of the random consistency ratio from Table 5.
7. Checking the consistency of the pair-wise comparison matrix to check whether the decision maker's comparisons were consistent or not. Figure 2: Dimensions of quality evaluation system. \(w\) is the eigenvector corresponding to the largest eigenvalue. Now, we find the consistency index, \(CI\), as follows:  
\[CI=\frac{\lambda_{max}-n}{n-1}=\frac{6.068-6}{6-1}=0.0136 \tag{5}\]  
Selecting an appropriate value of random consistency ratio \(RI\) for a matrix size of five using Table 5, we find \(RI=1.24\). We then calculate the consistency ratio \(CR\) as follows:  
\[CR=\frac{CI}{RI}=\frac{0.0136}{1.24}=0.011 \tag{6}\]  
As the value of \(CR\) is less than 0.1, the judgments are acceptable. Similarly, the pairwise comparison matrices and priority vectors for the remaining criteria can be found as shown in Tables 6-9, respectively. Regarding completeness, out of the 264,762 records, including 14,709 data items and 18 attributes, we obtained 67,839 records with null values. This accounted for approximately 25.62% of the data, resulting in a completeness score of 81.70. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \begin{tabular}{c} \(B1\) \\ **Integrity** \\ \end{tabular} & \begin{tabular}{c} \(B2\) \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} \(B3\) \\ **Timeliness** \\ \end{tabular} & \begin{tabular}{c} \(B4\) \\ **Consistency** \\ \end{tabular} & \
\begin{tabular}{c} \(B5\) \\ **Uniqueness** \\ \end{tabular} &
\begin{tabular}{c} \(B6\) \\ **Compliance** \\ \end{tabular} & \(w_{i}\) \\ \hline \(B1\) integrity & 1 & 2 & 1/2 & 3 & 3 & 2 & 0.229 \\ \(B2\) accuracy & 1/2 & 1 & 1/3 & 2 & 2 & 1 & 0.133 \\ \(B3\) timeliness & 2 & 3 & 1 & 4 & 4 & 3 & 0.364 \\ \(B4\) consistency & 1/3 & 1/2 & 1/4 & 1 & 1 & 1 & 0.084 \\ \(B5\) uniqueness & 1/3 & 1/2 & 1/4 & 1 & 1 & 1 & 0.084 \\ \(B6\) compliance & 1/2 & 1 & 1/3 & 1 & 1 & 1 & 0.106 \\  & & & \(\lambda_{max}=6.068\), \(CR=0.011<0.1\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quality evaluation system evaluation matrix \(A1\). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Size of Matrix** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Random consistency & 0 & 0 & 0.58 & 0.9 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 & 1.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average random consistency (RI). Concerning accuracy, among the 14,709 data records, we did not have any instances of data being updated or created later than the current time. Additionally, we had 5191 records with expired certificates. Roughly 35.29% of the data exhibited accuracy issues, resulting in an accuracy score of 91.58. In terms of timeliness, we did not identify any problems within the 14,709 data records analyzed, warranting a perfect timeliness score of 100. In terms of consistency, out of the 14,709 data records, 13,869 records belonging to the same information subject within the dataset did not meet the criteria for logical accuracy. This suggested that approximately 94.29% of the data exhibited consistency issues, resulting in a consistency score of 5.71. Regarding uniqueness, we did not find any instances of noncompliant record uniqueness among the 14,709 data records, resulting in a uniqueness score of 100.