In statistics, regression analysis refers to a statistical analysis method to determine the quantitative relationship between two or more variables. Regression analysis is divided into univariate regression and multivariate regression analysis according to the number of variables involved; According to the number of dependent variables, it can be divided into simple regression analysis and multiple regression analysis; According to the type of relationship between independent variables and dependent variables, it can be divided into linear regression analysis and nonlinear regression analysis. ?

Applicable conditions

In big data analysis, regression analysis is a predictive modeling technology, which studies the relationship between dependent variables (targets) and independent variables (predicted values). This technique is usually used for forecasting analysis, time series model and finding the causal relationship between variables. For example, the best way to study the relationship between reckless driving and the number of road traffic accidents is regression.

Attention problem

When applying regression prediction method, it is necessary to determine whether there is correlation between variables. If there is no correlation between variables, applying regression prediction method to these variables will get wrong results.

Attention should be paid to the correct application of regression analysis and prediction;

① Use qualitative analysis to judge the dependence between phenomena;

② Avoid arbitrary extrapolation of regression prediction;

③ Apply appropriate data.