The WARD (Weighted Average of Relative Deviation) method is a statistical technique used to measure the similarity or dissimilarity between different objects or groups. It is commonly used in cluster analysis to identify patterns and groupings among data points.
The WARD method calculates the distance between each data point and the center of its group, known as the centroid. It then combines these distances using a weighted average, giving more weight to closer points. This results in a measure of dissimilarity between each data point and its group, which is used to determine the optimal grouping of data points.
The 30° angle between W and the radial axis in a WARD plot represents the optimal grouping of data points. It indicates that there is a significant difference between groups, allowing for clear separation and identification of distinct clusters.
The 30° angle is calculated by plotting the dissimilarity values for each group on a WARD plot and finding the point where the lines intersect at a 30° angle. This point represents the optimal grouping of data points, with each group having a significant difference from the others.
The WARD method is advantageous because it is a simple and efficient way to group data points based on their similarity or dissimilarity. It also allows for clear visualization of clusters and can handle large datasets with multiple variables. Additionally, it can be applied to different types of data, making it a versatile tool for data analysis.