Datasets which are identical over a number of statistical properties, yet produce dissimilar graphs, are frequently used to illustrate the importance of graphical representations when exploring data. This paper presents a novel method for generating such datasets, along with several examples. Our technique varies from previous approaches in that new datasets are iteratively generated from a seed dataset through random perturbations of individual data points, and can be directed towards a desired outcome through a simulated annealing optimization strategy. Our method has the benefit of being agnostic to the particular statistical properties that are to remain constant between the datasets, and allows for control over the graphical appearance of resulting output.
Hence, statistics have a significance to understand the rates and the future tendency of suicides. There is no doubt that suicide is a global issue in all countries of the world as it is a leading cause of death alongside with cancer and heart disease. From this perspective, it seems vital to develop effective preventive approaches that will reduce the occurence of suicide worldwide. Likewise, some practical and evidence-based interventions are to be implemented to educate the general public about suicide and the methods to prevent it.