Research on the relationship between the Geochemical logging and Normal logging of Geophysics s data with the Multiple Regression Analysis method;

Four different methods,including support vector machine(SVM),artificial neural network(ANN),multiple regression analysis(MRA) and parameter product decision(PPD),were applied to the gassiness evaluation of forty gas-bearing layers in the tight sandstones of Tabamiao Area in Ordos Basin.

To probe into this correlation,the death rate per hundred thousand people and 14 indicators of economic and social development were studied in the selected 27 sample countries employing multiple regression analysis,through which a multiple regression model was established to relate the indicator of the death rate per hundred thousand peopl.

Prediction of higher-permeability region for CBM exploration based on multivariate regression analysis:A case study from Qinshui Basin;

Multivariate regression analysis of postoperative complications of resection of hepatocellular carcinoma;

The multivariate regression analysis method for landslide prediction;

Multiple regression analysis of ankle-brachial index risk factors in high-risk population with peripheral arterial disease;

Following the description of the inequality of China s urban per capita GDP,this paper explores the reason of the inequality through the multiple regression analysis.

Based on the multi-regression analysis of sinter quality data for resent three years, the influence rules of sinter chemical composition and micro-structure on sinter metallurgical properties in BAOGANG were found out.

The way how to build up the mathematics model of estimating tar content in finished product by multi-regression analysis is discussed here.

From studying the relationship between firm s social capital and S&T innovation,it puts out the concept model and theoretical hypotheses;it also analyzes the relationship between them through questionnaire investigation with a sample of 210 firms and by the method of multi-regression analysis.

Single factor analysis and multivariate logistic regression analysis were used for analysis of the factors.