In the above model, cracking increases with increase in CVPD and AGE, as they appear with positive coefficients. On the contrary, cracking decreases with increase in MSN, as it appears with negative coefficient. The negative sign indicates that the stronger the pavement, the lesser the potential for cracking. In order to explain the robustness of the model, a plot is made between observed cracking values of the out of sample data and predicted cracking values from the model as shown in Figure 2. The alignment of plotted points along the line of equality indicates the robustness of the model.
KNN imputation method seeks to impute the values of the missing attributes using those attribute values that are nearest to the missing attribute values. The similarity between two attribute values is determined using the distance function. In brief, the KNN computation method is used to predict the missing values in the dataset. It can be fine to say that it is used as a replacement for the traditional imputation techniques.
Missing Values Spss Modeler Crack
After excluding responses with more than 30% missing values in HSPSC-D items, we conducted multiple imputations based on the expectation maximisation (EM) algorithm using the statistical software NORM V.2.0320 21 to replace remaining missing values. Negatively worded items were reverse coded before further analysis.
Out of our sample of n=995, 766 responses (76.98%) had no missing values on HSPSC items. Twenty-one responses (2.1%) contained more than 30% missing values on HSPSC items and were thus not included in the analysis. Remaining missing values were imputed using multiple imputations based on the EM algorithm. As a result, n=974 cases were available for further analysis. Descriptive statistics of HSPSC-D items and dimensions after imputing remaining missing answers and reverse coding of the negatively worded items are presented in table 2. 2ff7e9595c
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