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Non-parametric methods analysis is an important part of statistical analysis methods, which is the basic content of statistical inference together with parameter test. The parameter test is a method to infer the parameters such as mean, variance, etc. of the general distribution in the case of a known general distribution. However, in the process of data analysis, because of various reasons, people often cannot make a simple assumption about the overall distribution pattern. At this time, the method of parameter test is no longer applicable. Non-parametric testing is a kind of method based on this consideration, in which the total variance is unknown or little known, and the sample data is used to infer the overall distribution pattern. Since nonparametric test methods do not involve the parameters of the general distribution in the inference process, they are named as "Nonparametric" tests. In practical application, because nonparametric test does not need to pre-set the specific form and error distribution of the model, it can obtain a wide nonlinear change. At the same time, in the overall evaluation of the sample, nonparametric test is not necessary to rely on the overall distribution of the sample. It can be widely used in different types of the general, which is conducive to reduce the deviation and improve the understanding of the dynamic structure of a sample sequence. Non-parametric Methods Analysis
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