First of all, the premise:
Due to the complexity of the internal laws of objective things and the limitations of people's understanding, it is impossible to analyze the internal causal relationship of actual objects and establish a mathematical model that conforms to the mechanism laws.
Therefore, when some mathematical models cannot be established by mechanism analysis, the method of collecting a large number of data is usually used to establish models based on statistical analysis of data, and the most widely used stochastic model is statistical regression model.
Second, the practical significance:
1. Market analysis: Multiple regression model can be used to analyze the market performance, so as to realize effective portfolio management.
2. Price prediction: Multiple regression model can be used to predict the changes of commodity prices, so as to better meet the needs of consumers.
3. Consumer behavior analysis: Multiple regression model is helpful to study the consumer behavior of respondents under different independent variables and provide decision support for enterprises.
Applying multiple regression model method:
1, data preprocessing:
Firstly, it is required to obtain effective original data, including the values of independent variables and dependent variables, and clean and preprocess these original data to ensure the effectiveness of the model.
2, variable analysis:
In multiple regression, there is a complex relationship between independent variables and dependent variables. Therefore, only by systematic analysis and statistical test of variables can we understand the relationship between them and accurately build a multiple regression model.
3. Build a model:
When using multiple regression model to model, we should choose the appropriate model according to different data characteristics and analysis purposes to meet the requirements of analysis to the greatest extent.
4. Model verification:
When using multiple regression model to analyze data, we should not only consider the accuracy of the model, but also pay attention to the verification of the model to ensure the effectiveness and accuracy of the model.