Quantitative infrared spectrum detection of SF6 decomposition components based on principal component regression analysis

2017 ◽  
Author(s):  
Yang Yuling ◽  
Guo Xinliang ◽  
Cui Zhaolun ◽  
Xia Huanhuan ◽  
Yang Xueying ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Infrared Spectrum ◽  
Principal Component Regression ◽  
Principal Component ◽  
Spectrum Detection ◽  
Principal Component Regression Analysis ◽  
Sf6 Decomposition

Habitat–abundance relationships of the earthworm Eisenia rosea (Savigny) (Lumbricidae), using principal component regression analysis

1994 ◽  
Vol 72 (7) ◽  
pp. 1354-1361 ◽  
Author(s):  
Qiwei Liang ◽  
Alan J. Thomson
Keyword(s):  
Regression Analysis ◽  
Principal Component Regression ◽  
Principal Component ◽  
Soil Bulk Density ◽  
Soil Characteristics ◽  
Environmental Data ◽  
Data Matrix ◽  
Step Procedure ◽  
Principal Component Regression Analysis ◽  
Main Factor

Principal component regression analysis was used to investigate the relationships between the abundance of the earthworm Eisenia rosea and soil characteristics at two Ontario locations. To this end we summarized our environmental data matrix with principal component analysis and then used the first several principal components in a multiple regression analysis. This two-step procedure remedies problems associated with multicollinearity among our environmental variables. At one location, moisture was the main factor correlating with the abundance of E. rosea. At the other location, because high soil bulk density can compensate for low moisture, E. rosea abundance did not correlate with moisture.

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