Cluster analysis refers to the analysis process of grouping the collection of physical or abstract objects into multiple classes composed of similar objects. Clustering is the process of classifying data into different classes or clusters, so the objects in the same cluster are very similar, while the objects in different clusters are very different. Cluster analysis is an exploratory analysis. In the process of classification, people don't have to give a classification standard in advance, and cluster analysis can automatically classify from sample data. Different methods used in cluster analysis often lead to different conclusions. Different researchers do cluster analysis on the same set of data, and the number of clusters obtained may not be consistent.

2. Factor analysis

Factor analysis refers to the statistical technique of extracting gender factors from variable groups. Factor analysis is to find internal relations from a large number of data and reduce the difficulty of decision-making. There are many methods of factor analysis, such as 10, such as gravity center method, image analysis method, maximum likelihood method, least square method, alpha extraction method, Rao typical extraction method and so on. Most of these methods are approximate methods based on correlation coefficient matrix. The difference is that the diagonal value of the correlation coefficient matrix is estimated by different * * * same sex □2. In sociological research, the iterative method based on principal component analysis is often used in factor analysis.

3. Correlation analysis

Correlation analysis, correlation analysis is to study whether there is a certain dependence between phenomena, and to explore the relevant direction and degree of specific phenomena with dependence. Correlation is an uncertain relationship. For example, if X and Y are used to record a person's height and weight respectively, or to record the amount of fertilizer applied per hectare and the yield of wheat per hectare respectively, then X and Y are obviously related, but not to the extent that one of them can accurately determine the other. This is relevance.

4. Correspondence analysis

Correspondence analysis, also known as correlation analysis and R-Q factor analysis, reveals the relationship between variables by analyzing the interactive summary table composed of qualitative variables. It can reveal the differences between the categories of the same variable and the corresponding relationship between the categories of different variables. The basic idea of correspondence analysis is to express the proportional structure of each element in the row and column of the linked list in the form of points in the low-dimensional space.

5. Regression analysis

Study the influence of a random variable Y on another (x) or a population (X 1, X2,? , Xk) statistical analysis method of variable correlation. Regression analysis is a statistical analysis method to determine the quantitative relationship between two or more variables. It is widely used. Regression analysis can be divided into univariate regression analysis and multivariate regression analysis according to the number of independent variables involved. According to the type of relationship between independent variables and dependent variables, it can be divided into linear regression analysis and nonlinear regression analysis.