1. Descriptive statistical analysis: This is the most basic statistical analysis method to describe the basic characteristics of data, such as mean, median and standard deviation.
2. Inference statistical analysis: This method is used to infer the overall characteristics from the sample data. Common statistical analysis methods of inference include t-test, chi-square test, variance analysis and regression analysis.
3. Multivariate statistical analysis: This method is used to deal with the relationship between multiple variables. Common multivariate statistical analysis methods include principal component analysis, factor analysis, cluster analysis and discriminant analysis.
4. Time series analysis: This method is used to process time-varying data. Common time series analysis methods include autoregressive model, moving average model and autoregressive moving average model.
5. Bayesian statistical analysis: This method is based on Bayesian theorem and used to deal with uncertainty. Common Bayesian statistical analysis methods include Bayesian network, Markov chain Monte Carlo method and so on.
6. nonparametric statistical analysis: this method does not depend on the distribution assumption of data, and is suitable for nonlinear and non-normal data. Common nonparametric statistical analysis methods include rank sum test, Kruskal-Wallis test, Spearman rank correlation and so on.
7. Survival analysis: This method is used to process survival data, such as death time, disease recurrence time, etc. The common survival analysis methods are Kaplan-Meier method and Cox proportional hazard model.
8. Machine learning and artificial intelligence: These methods are used to process large-scale complex data, such as neural networks, support vector machines, random forests, etc.
These are just some commonly used data analysis and statistical methods. In fact, there are many other methods and technologies that need to be selected according to specific research problems and data characteristics.