multiple linear regression

In this research, Quantitative Structure–Activity Relationship (QSAR) studies have been used to predict activities of organochlorine pesticides. Firstly, the chemical structure of molecules was drawn with the Gauss view 05 program and optimized at Hartree–Fock level of theory and 6-31G* basis sets using Gaussian 09 software. The physiochemical properties namely octanol-water partition coefficient (logP) and toxicity (log LD50) are taken from the scientific web book. The dragon software has been used for the calculation of molecular descriptors. The suitable descriptors were selected with the aid of the genetic algorithm (GA) and backward techniques. At the next step, the relationship between molecular descriptors and the activities was investigated by multiple linear regression (MLR) method. In order to build and test QSAR models, a data set of organochlorine pesticides was randomly separated into 2 groups: training (80%) and test (20%) sets. The models were evaluated with regression parameters: correlation coefficient (R), squared regression coefficient (R2), adjusted correlation coefficient (R2 adj) and root mean squared error (RMSE). For the predictive ability and verification of the models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The external prediction accuracy of the obtained models was examined using the above regression parameters. Results of validations and high statistical quality of models indicate that generated GA-MLR models are reasonable QSAR models. These models help to delineate the important descriptors responsible for predicting their activities.

Pesticides are considered as one of the most significant environmental pollutants. The transfer of pesticides into the living organism and the maximum residue limit of pesticides is one of the main environmental concerns. Pesticides have got the attention due to their various applications in biochemistry, environment and agriculture. Consequently, the physical and chemical characteristics of pesticides particularly the pesticides toxicity needs to be taken into consideration. The aim of this research is investigation the relationship between Log P of some pesticides with topological descriptors by graph theory and multiple linear regression methods. Quantitative structure-property relationship study was used and results was showed that Platt, Harary, Randic, Szeged indices are suitable for prediction of Log P the pesticides than the other topological indices. The best model in this study indicated that those structural descriptors, play an important role in effect on Log P of pesticides. For the first time, the relationship between Log P of pesticides and some topological indices using SPSS and multiple linear regression model is investigated.

In order to study the weed damage and to determinate the influence of weed species and multiple weed species competition in corn (Zea mays L.), a field experiment was conducted based on interval mapping at the Agronomy Research Field of Ferdowsi University of Mashhad during 2009-2010. Treatments consisted of four density level of crop (5, 6, 7 and 9 plant.m-2) and four type of weed management (completely removed weed, broadleaf control, non control, and grass weeds control). Weed samples were taken at harvest time. Dry matters of corn samples were measured. The Density and dry matter of weed samples were recorded separately for each species. Equations were fitted single plant weight ln of each weed species or crop as dependent variable to compare and evaluate the competition coefficient of inter and intra species. Results indicated that single plant weight ln and plant density per m-2 had high correlation to evaluate competition coefficient. Considering the coefficients of equation the function of single plant weight ln showed that crab grass and prostrate pigweed had the most stimulation effect on corn. While redroot pigweed prostate common purslane field bindweed black night shade common lambsquars barnyardgrass purple grown nutsedge and Green foxtail had inhibition effect on corn. The positive of the former group on corn were resulted from the inhabitation effect on the latter group of weeds.