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  | pdf    = http://archives.datapages.com/data/specpubs/methodo1/images/a095/a0950001/0300/03430.pdf
 
  | pdf    = http://archives.datapages.com/data/specpubs/methodo1/images/a095/a0950001/0300/03430.pdf
 
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[[File:Correlation-and-regression-analysis fig1.png|thumb|{{figure number|1}}Linear regression of x-on-y. Note the negative slope corresponding to a negative correlation. The regression line is determined so as to minimize the sum of squared deviations: [equation]]]
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Correlation analysis, and its cousin, regression analysis, are well-known statistical approaches used in the study of relationships among multiple physical properties. The investigation of [[permeability]]-[[porosity]] relationships is a typical example of the use of correlation in geology.
 
Correlation analysis, and its cousin, regression analysis, are well-known statistical approaches used in the study of relationships among multiple physical properties. The investigation of [[permeability]]-[[porosity]] relationships is a typical example of the use of correlation in geology.
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The fitting of surfaces by least squares is an important component in most automated contouring software packages and is commonly used in computer generation of geological maps. Trend surface analysis, another mapping technique, is also based on the principles of least-squares fitting. Finally, some of the more specialized geostatistical techniques, such as kriging, are likewise rooted in the basic principles of least squares and multiple regression.
 
The fitting of surfaces by least squares is an important component in most automated contouring software packages and is commonly used in computer generation of geological maps. Trend surface analysis, another mapping technique, is also based on the principles of least-squares fitting. Finally, some of the more specialized geostatistical techniques, such as kriging, are likewise rooted in the basic principles of least squares and multiple regression.
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[[File:Correlation-and-regression-analysis fig1.png|thumb|Linear regression of x-on-y. Note the negative slope corresponding to a negative correlation. The regression line is determined so as to minimize the sum of squared deviations: [equation]]]
      
==References==
 
==References==

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