# Weight Assignment Method and Application of Key Parameters in Shale Gas Resource Evaluation
## 4 Weight Conversion Model  
To conduct research on resource estimation and related key parameter weight assignment, a weight transformation model is proposed, which uses the correlation coefficient representing the relationship between different variables and the complex correlation coefficient representing the correlation degree between a set of random variables and a random variable in the field of statistics to characterize the correlation degree between different parameters and resource calculation results. The correlation coefficient is a statistical indicator used to reflect the degree of correlation between variables, usually represented by the letter r. Due to different research subjects and purposes, there are multiple correlation coefficients in the field of statistics. The complex correlation coefficient is an indicator that reflects the degree of correlation between a random variable and a group of random variables (two or more). It is a comprehensive measurement index that includes all variables and is generally used for measuring the degree of correlation between many variables to one variable. The complex correlation coefficient ranges from 0 to 1 (0 \(\leq R_{y\cdot 1\cdot 2\cdot k}\leq\) 1). The larger the complex correlation coefficient, the closer the correlation between elements (variables). The complex correlation coefficient of 1 indicates a complete correlation; the complex correlation coefficient of 0 indicates complete independence. At the same time, each factor also has its partial regression coefficient, which means that the average change in the dependent variable Y when the independent variable Xi changes by one unit, while keeping other independent variables constant, can measure the individual correlation of a certain factor. The F-test, initially known as the Variance Ratio or Joint Hypotheses Test, was proposed by British statistician [PERSON]. Initially, it mainly compared the variance of two sets of data to determine whether there was a significant difference in their density. It reflects whether the linear relationship between the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Source of Weight** & **Basic Evaluation Method** & **Remarks** \\ \hline \multirow{4}{*}{Experience weight} & Expert scoring method & Determine weights based on relative importance of evaluation indicators \\ \cline{2-3}  & Analytic hierarchy process & Provide quantitative representations based on relative importance of various factors at different levels \\ \hline \multirow{4}{*}{Statistical weight} & Nemerow index & Calculate comprehensive index based on mean and extreme values of evaluation indicators \\ \cline{2-3}  & Grey correlation analysis method & Calculate correlation degree based on similarity of geometric shapes of curves formed by numerical sequences \\ \cline{2-3}  & Correlation method & Determine indicator weights based on correlation coefficients to reflect sensitivity of indicators \\ \cline{2-3}  & Principal component analysis & Screen based on cumulative contribution rate of variance and determine weights based on common factor variance \\ \hline Experience weight and statistical weight & Comprehensive weighting method & Balance experience weight and statistical weight \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Existing weight assignment methods. -dependent variable and all explanatory variables in the multiple linear regression model is significant overall [36]. In the practical application of weight conversion models, the first step is to determine whether the selected multiple parameters are relevant or effective and to test their correlation through significance testing to avoid false correlations caused by data coincidences. Next, the partial correlation coefficients of the key parameters are obtained to determine their correlation, and multiple regression is used to fit them. This can determine the degree of correlation between numerous parameters and resource levels and assign different weights to the corresponding parameters.