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Process Help CORREL - calculate bivariate statistics for numeric fields in a file |
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Process Name |
Menu Path |
Link to Command Table |
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CORREL |
Introduction
Calculates bivariate statistics on pairs of numeric fields in a file. All results are output as the bottom left-hand triangle of a full two-dimensional matrix. Output is written to the display, and includes the following:-
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a summary of the mean and variance of each numeric field.
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sample number matrix.
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correlation matrix.
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covariance matrix.
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F ratio matrix.
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paired t test matrix.
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More information about this command can be found on Datamine's online Knowledge Base:https://datamine.freshdesk.com/en/support/solutions/articles/19000084719-correl-calculation-of-bivariate-statistics-for-numerical-fields |
Statistics
Correlation Coefficient
The correlation coefficient is a number between -1 and 1 which measures the degree to which two variables are linearly related. If there is perfect linear relationship with positive slope between the two variables, we have a correlation coefficient of 1; if there is positive correlation, whenever one variable has a high (low) value, so does the other. Further details are available from:
http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
http://www.stats.gla.ac.uk/steps/glossary/paired_data.html
Covariance
Covariance provides a measure of the strength of the correlation between two or more sets of random variables. For example between Au and Ag in a set of samples. Further details are available from:
http://mathworld.wolfram.com/Covariance.html
F Ratio Test
The F Ratio test determines whether the variances of two sets of measured values with different numbers of samples are significantly different from each other. For example to compare the variance of Au grades for samples in rock types A and B . Further details are available from:
http://www.acastat.com/Handbook/14.html
http://www.itl.nist.gov/div898/handbook/eda/section3/eda3673.htm
Paired t Test
The paired t test determines whether two paired sets of measured values are significantly different from each other. For example if a set of samples are analysed by two laboratories and you want to test whether there is a difference between them. Further details are available from:
http://www.itl.nist.gov/div898/handbook/prc/section3/prc311.htm
http://udel.edu/~mcdonald/statpaired.html
http://mathworld.wolfram.com/Pairedt-Test.html
Pooled t test
The pooled t test determines whether two sets of measured values with different numbers of samples are significantly different from each other. For example to compare the mean Au grade for samples in rock types A and B . Further details are available from:
http://www.itl.nist.gov/div898/handbook/eda/section3/eda353.htm
http://www.stat.yale.edu/Courses/1997-98/101/meancomp.htm
Files, Fields and Parameters
Input Files
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Name |
Description |
I/O Status |
Required |
Type |
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IN |
Input file. |
Input |
Yes |
Undefined |
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FIELDLST |
File to supply selected fields. |
Input |
No |
Undefined |
Output Files
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Name |
I/O Status |
Required |
Type |
Description |
Fields
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Name |
Description |
Source |
Required |
Type |
Default |
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F1 |
First field to be correlated. No fields specified means all. |
IN |
No |
Numeric |
Undefined |
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F2 |
Second field to be correlated. |
IN |
No |
Numeric |
Undefined |
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F3 |
Third field to be correlated. |
IN |
No |
Numeric |
Undefined |
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F4 |
Fourth field to be correlated. |
IN |
No |
Numeric |
Undefined |
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F5 |
Fifth field to be correlated. |
IN |
No |
Numeric |
Undefined |
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F6 |
Sixth field to be correlated. |
IN |
No |
Numeric |
Undefined |
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F7 |
Seventh field to be correlated. |
IN |
No |
Numeric |
Undefined |
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F8 |
Eighth field to be correlated. |
IN |
No |
Numeric |
Undefined |
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F9 |
Ninth field to be correlated. |
IN |
No |
Numeric |
Undefined |
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F10 |
Tenth field to be correlated. |
IN |
No |
Numeric |
Undefined |
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FIELDNAM |
Field in FIELDLST to identify selected fields. |
FIELDLST |
No |
Character |
Undefined |
Notes
The sample number matrix contains the number of pairs of samples for each pair of fields. This shows when one or other of the fields has absent data values. If none of the records contain missing data then all the elements of the matrix will contain the same value.
The mean and variance of each field shown in the summary statistics are calculated using all the available data, whereas the matrices are calculated using only data where neither sample is missing. Thus if there is missing data it is not possible for example to calculate the correlation coefficient from the covariance and variances. Bivariate statistics are calculated by default for all pairs of numeric fields. Thus if for example the data file contains sample co-ordinates statistics will be calculated for the X and Y co-ordinates.
Example
!CORREL &IN(SAMPLES)
Error and Warning Messages
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Message |
Description |
Solution |
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ERR 122 <<< ( fileno) IN CORREL |
No numeric fields in file, or fields specified were not numeric. Fatal; the process is terminated. |
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ERR 121 <<< ( fileno) IN CORREL |
File read error. Fatal; the process is terminated. |
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