英文翻译具有学术风格多用高级词汇不要漏翻译：对于上述变量从F检验的结果分析可以得到显著性P值为0000水平上呈现显著性均对Listing Price USD有显著性影响存在主效应可以进行后续检验。 52 Model building based on five methods 相关性分析后我们筛选出了剩下的不同水平下显著相关的数据使用不同的回归拟合方法拟合这些数据和ListingPrice的关系

Based on the results of the F-test analysis for the aforementioned variables, the significance P-value was found to be 0.000***, indicating a significant level of influence on the Listing Price (USD) variable. These variables exhibit main effects and can be subject to further testing.

After conducting a correlation analysis, we selected the remaining data with significant correlations at different levels and used various regression fitting methods to model the relationship between these variables and the Listing Price. The fitting methods used in this study were the bp neural network, random forest, multiple linear regression, svm regression, and decision tree methods. Due to space limitations, this paper only provides a detailed description of the first two methods. We used the mean squared error (MSE) and coefficient of determination (R²) as measures of the fitting quality of the regression models. The MSE represents the average of the squared difference between actual and predicted values, and a smaller value indicates better predictability of the model. The R² represents the percentage of variation in the predicted value compared to the actual value, with values ranging from 0 to 1. A value closer to 1 indicates a better fit of the model. Specifically, R² is used to compare the effectiveness of a given model with a simple average value. When R² is equal to 1, the model is as effective as using a simple average value for prediction. When R² is 0, the model cannot explain the variability of the target variable. The results of the calculations will be summarized in the conclusion of this chapter.

For the fitting of multi-dimensional data X and actual results Y, we need to obtain the parameters of the fitting function and predict the Y value of new samples. Neural networks have strong fitting capabilities and can gradually approach the real result Y by training and adjusting their parameters adaptively. Unlike traditional modeling methods based on physical principles, neural networks can directly learn the underlying rules from data without any prior knowledge. The bp neural network is the most basic network structure, consisting of an input layer, a hidden layer, and an output layer. The input layer receives the raw data X, the hidden layer performs non-linear transformations on the data, and the output layer provides the final predicted result. The specific structure is shown in the following figure: