Unary linear regression is a method to analyze the linear correlation with only one independent variable (independent variable X and dependent variable Y). The value of an economic indicator is often influenced by many factors. If only one of them is the main decisive factor, one-dimensional linear regression can be used for prediction and analysis.

Linear regression is a statistical analysis method that uses regression analysis in mathematical statistics to determine the quantitative relationship between two or more variables.

The expression of linear regression is y= w'x+e, where e is a normal distribution with an average value of 0. Linear regression analysis only includes one independent variable and one dependent variable, and the relationship between them can be approximately expressed by a straight line. This regression analysis is called univariate linear regression analysis. If regression analysis contains two or more independent variables, and there is a linear relationship between dependent variables and independent variables, it is called multivariate linear regression analysis.

Linear regression analysis is often used to solve practical problems such as prediction, control and causal analysis. For example, in the commercial field, the sales volume of products can be predicted by linear regression analysis; In the medical field, the probability of disease occurrence can be predicted by linear regression analysis; In the field of economics, the inflation rate can be predicted by linear regression analysis.

Linear regression analysis has some limitations, for example, it assumes that there is a linear relationship between data, while the actual data often presents a nonlinear relationship. Therefore, it is necessary to carefully consider the applicability and limitations of linear regression analysis.

Matters needing attention in the application of sexual regression analysis;

1 First, it is necessary to make clear that there is a linear relationship between the dependent variable and the independent variable, which is the premise of linear regression analysis. If there is no linear relationship, then linear regression analysis may get inaccurate results.

2. In linear regression analysis, it is necessary to use appropriate data processing methods to reduce the influence of errors and noises on the results, such as data cleaning and outlier processing.

3. The linear regression analysis may be influenced by multiple linearities, which will lead to the instability of the model and the decline of the prediction accuracy. Therefore, when selecting independent variables, it is necessary to carry out correlation and multiple * * * linear tests to avoid this situation.

4. When applying linear regression analysis, we need to consider the distribution and characteristics of data, as well as the scope and limitations of the model. If the data does not obey the normal distribution or the model is not suitable for a specific data set, then the linear regression analysis may get inaccurate results.

5. Finally, we should use appropriate statistical methods and indicators to evaluate the results of linear regression analysis, such as determination coefficient, t test, f test, etc.