<div style="text-align: justify;">In fisheries science, high number of morphometric measures (independent variables) taken from different parts of the fish complicates the estimation of the body weight (dependent variable). Therefore, the researchers are seeking for a solution facilitating the interpretation of the equations of correlation between the characteristics. One way to deal with this challenge is the dimension reduction by means of stepwise multiple regression analysis. The aim of this study is to explain total variation with the same accuracy by using fewer independent variables. To accomplish this, 12 morphometric measures from 210 individuals of Serranus cabrilla were measured to estimate the body weight. Firstly, the 95% of the variation was explained by means of multiple regression analysis by using all variables. Then, by step-wise method, the same results were achieved with fewer independent variables. Finally, the variables with inter-multicollinearity eliminated and with two remaining independent variables determination coefficients resulted as 95%. &nbsp;The result showed that using more variables does not create significant distinction for accuracy to estimate the body weight although; the total length and body dept was the most effective features for weight.&nbsp;</div>

Application of multiple regression analysis to morphometric characters obtained from <em>Serranus cabrilla </em>(linnaeus, 1758) by using stepwise method<br />&nbsp;

<div style="text-align:justify"><strong>Background:</strong> Identification of suitable factors that influence significantly to the response is crucial for the traits based breeding program to make a better decision about improvement in productivity. Multiple linear regression (MLR) is the benchmark method commonly using to identify suitable factors for crop improvement. It doesn&rsquo;t work always due to stringent assumption (Multicollinearity, Linearity) behind the MLR model. Here we tried to develop an efficient model for the selection of major traits that contribute to seed yield in soybean by comparing different models.<br />
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<strong>Methods: </strong>Field experiment was conducted using 98 soybean core population through augmented design.18 morphometric traits obtain from soybean core population were considered under the study as regressors.Multiple linear regression (MLR), Principle component Regression (PCR), Regression tree and Random Forest models were compared to select traits based on prediction accuracy.<br />
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<strong>Result: </strong>All the models identified the number of pods per plant (NPP) has the most influencing variable to the soybean yield. However random forest has a much higher prediction power (RMSE=4.59, MAPE=0.18) compared to other models under study. The results of random forest revealed that the number of pods per plant, number of branches per plant and other associated characters like plant height at harvest as highly influencing traits for seed yield in soybean.Finally, tried to identify genotypesthat possess superiority about most influencing morphological characters on seed yield using cluster analysis.</div>


<div style="text-align:justify">Trait Based Modelling Approach for Selection of Elite Germplasm Accessions in Soybean [<em>Glycine max </em>(L). Merrill]</div>


<div><strong>Background:&nbsp;</strong>Live body weight (BW) of livestock animals is truly mirror image of all activities of genetics, nutrition, production, reproduction and health status. Thus, the knowledge of calculating BW is of great importance to the producer and critical for goat farming and business. However, there is an unavailability of suitable scales, leading to inaccuracies in decision-making. The present work aimed to predict the live BW of Indian Black Bengal goat using certain morphometric data.&nbsp;<br />
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<strong>Methods:&nbsp;</strong>The live BW and eight body measurement data from 1427 disease free, non-pregnant goats aged 25.87&plusmn;10.47 months with 2.78&plusmn;1.21 number of parity were collected. The data were first subjected to stepwise regression analysis to achieve the best-fitted model for BW prediction by comparing coefficient of determination (R<sup>2</sup>) and determining the combination of body dimensions that explained variation in the dependent variable. Further, Recursive Partitioning and Regression Trees (RPART) model, a machine learning tool was deployed to predict BW using certain body measurements.<br />
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<strong>Result:&nbsp;</strong>The results of stepwise regression model clearly indicated that heart girth (HG) and punch girth (PG) measurements influenced live BW mostly, but the predictive capabilities (Low R<sup>2</sup>) of this statistical model were low. The stepwise regression model could not satisfactorily predict BW due to the problem of multicollinearity. Out of eight independent variables, the most important variables emerged from RPART were only HG and PG based on the largest reduction in overall sums of squares error. RPART generated a decision tree with minimal expected error to precisely predict live BW. Hence, RPART model was found to provide better predictive result than stepwise regression model in accurately predicting BW from body measurement variables in Black Bengal goats.</div>


<p>Body Weight Prediction using Recursive Partitioning and Regression Trees (RPART) Model in Indian Black Bengal Goat Breed: A Machine Learning Approach</p>


Application of multiple regression analysis to morphometric characters obtained from Serranus cabrilla (linnaeus, 1758) by using stepwise method

Trait Based Modelling Approach for Selection of Elite Germplasm Accessions in Soybean [Glycine max (L). Merrill]

Body Weight Prediction using Recursive Partitioning and Regression Trees (RPART) Model in Indian Black Bengal Goat Breed: A Machine Learning Approach