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The CHAID algorithm can project interactions between variables and non-linear effects, which are generally missed by traditional statistical techniques.
This relatively new technology helps geographers address issues which have been poorly handled by traditional statistical techniques in the past, such as scale, space time dependencies, nonlinear relationships, and data outliers.
Some of these classifiers can be used to extract relevant information in order to obtain attributes, and this process can be carried out by traditional statistical techniques to compare values using the parametric or not parametric test ANOVA [ 35], Kruskal-Wallis [ 36], and Mann-Whitney U test [ 37] or testing to compare the frequencies as parametric Chi squared [ 38] or Fisher's exact test.
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Consequently, traditional statistical techniques cannot contribute significantly to analyze the risk data.
Modeling MCDM requires the simultaneous consideration of multiple criteria but traditional statistical techniques can only evaluate these factors independently.
Data mining techniques [15, 16] have certain advantages over traditional statistical techniques.
However, [13, 14] shown that traditional statistical techniques has certain limitations in analyzing road accident data.
Various studies on road accident data analysis used traditional statistical techniques [2, 5 9] and data mining techniques [3, 10 14, 18].
Various studies have been done on road accident data analysis using traditional statistical techniques and data mining techniques.
Further, several studies using data mining techniques in road accident analysis has shown that data mining provides productive results than traditional statistical techniques.
Data mining techniques do not require certain assumptions between dependent and independent variables which are required in traditional statistical techniques [7].
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