Dataset: pesticide.csv

Source: K. Amirat, N. Ziani, D. Messadi (2016). "Chemometric Modeling to Predict Retention Times
for a Large Set of Pesticides or Toxicants Using Hybrid Genetic Algorithm/Multiple Linear Regression
Approach," Management of Environmental Quality: An International Journal, Vol. 27, #3, pp. 313-325.

Description: 
Regression model to predict log(Retention Time) to 6 Predictors for 84 pesticides.
Predictors:
Total Energy of Molecule
Number of 6-membered rings
Broto-Moreau autocorrelation of topological structure - lag 1 weighted by atomic masses
Broto-Moreau autocorrelation of topological structure - lag 7 weighted by Van der Waals volumes
Geary autocorrelation - lag 2 weighted by atomic Sanderson electronegativities
Eigenvalue 05 from edge adjacent matrix weighted by dipole moments
Note: First 67 pesticides were training sample, last 17 were test sample

Variable Names:
pestID      (1-84)
pestName    (chemical name)
logRT
totEnergy
num6Ring
ATS1m
ATS7v
GAATS2e
EEig05d
crossval   (1=training, 2=test)