scholarly journals Use of factor scores in multiple regression analysis for estimation of body weight by certain body measurements in Romanov Lambs

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7434 ◽  
Author(s):  
Yalcin Tahtali
Keyword(s):  
Factor Analysis ◽  
Body Weight ◽  
Regression Model ◽  
Multiple Regression ◽  
Prediction Equation ◽  
Multiple Regression Model ◽  
The Body ◽  
Body Measurements ◽  
Factor Scores ◽  
Two Factors

The study investigates the solution of the multicollinearity between certain body measurements of Romanov lambs and prediction of the body weight of Romanov lambs using the thus calculated factor analysis scores and a multiple regression model. For this purpose, the body measurements (wither height (WH), croup height (CH), body length (BL), chest depth (CD), chest circumference (CC), chest width behind shoulders (CWS) and head length (HL)) and body weight (BW) of 6-month-old 50 Romanov lambs born in 2015 were used. The factor analysis scores were used to obtain the prediction equation for the relationship between the investigated traits. The analysis results showed that there was a multicollinearity between the wither and croup height traits used in the prediction equation. Moreover, the results revealed that the variables for the body measurements can be represented by two factors. These factors explained 50.89% and 22.86% of the total variance, respectively. The multicollinearity between the independent variables was eliminated with the use of the factor scores obtained with the factor analysis in the multiple regression model, and thus it was observed that better results can be obtained by using the factor analysis scores in the prediction of the body weight of 6-month-old Romanov lambs.

scholarly journals Aplicación de la ingeniería kansei con redes neuronales

2015 ◽  
Vol 1 (1) ◽  
pp. 7
Author(s):  
Gerardo Avendaño Prieto ◽  
Héctor René Álvarez L.
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Factor Analysis ◽  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Kansei Engineering ◽  
Statistical Assumptions ◽  
The Mathematical Model

ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍALa Ingeniería Kansei, relaciona las emociones que sienten los consumidores con las características y propiedades que poseen los productos a través de la estimación de un modelo matemático, que permite establecer cuáles tienen mayor relación con las emociones, permitiendo al diseñador, incorporarlas con el fin de activar los factores que las intensifican y dar soluciones efectivas de diseño.Tradicionalmente, la formulación y estimación del modelo matemático, se hace través de un modelo de regresión múltiple (QT1) y análisis factorial; sin embargo, una de las desventajas es que está condicionado a cumplir los supuestos del modelo. En este trabajo, se muestra cómo las redes neuronales se pueden aplicar en los estudios de Ingeniería Kansei y nos  da resultados similares, permitiendo que se puedan utilizar cuando no se cumplen los supuestos estadísticos. ABSTRACT Kansei Engineering (emotions) relates that consumers fee/ with features and properties that have the products through the estimation of a mathematical model, which allows for properties which are high relative to the emotions, al/owing the designer incorporate these relations to activate factors which enhance the Kansei design and give effective solutions. Traditionally the development of the mathematical model and estimation is done through a multiple regression model (QT1) and factor analysis, one of the disadvantages of the estimation of this model is that is conditioned to meet the model assumptions. This paper shows how neural networks can be applied in studies of Kansei Engineering and gives similar results, allowing for use when no statistical assumptions are met.

scholarly journals Regression model analysis for prediction of body weight from body measurements in female Batur sheep of Banjarnegara District, Indonesia

2021 ◽  
Vol 22 (7) ◽  
Author(s):  
Alek Ibrahim ◽  
Wayan Tunas Artama ◽  
I Gede Suparta Budisatria ◽  
Ridwan Yuniawan ◽  
Bayu Andri Atmoko ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Model ◽  
Akaike Information Criterion ◽  
Computer Software ◽  
Correlation Coefficients ◽  
Information Criterion ◽  
Model Analysis ◽  
The Body ◽  
Body Measurements ◽  
Central Java

Abstract. Ibrahim A, Artama WT, Budisatria IGS, Yuniawan R, Atmoko BA, Widayanti R. 2021. Regression model analysis for prediction of body weight from body measurements in female Batur sheep of Banjarnegara District, Indonesia. Biodiversitas 22: 2723-2730. Bodyweight is an important aspect of livestock management. The present study was undertaken to estimate correlation coefficients between biometric traits and identify best predictor of body weight in female Batur sheep from body measurements. Data on body weight and body measurements (body length: BL, chest girth: CG and withers height: WH) were collected from 73 female Batur sheep in Batur Village, Banjarnegara District, Central Java Province, Indonesia. Batur sheep were grouped into 3 categories based on their age, namely groups <1.5 years, 1.5-2.5 years and >2.5 years. The data were analyzed using simple, multiple, and automatic linear regression methods using the SPSS computer software version 25 platform. The correlation coefficient, coefficient determination, adjusted coefficient determination, residual standard error, Akaike information criterion, Bayesian information criterion, and Akaike information criterion corrected were used to determine the best regression formula for the prediction of BW. The average BW (kg), BL (cm), CG (cm), and WH (cm) of 49.27, 63.11, 91.41, and 56.82, respectively was observed in the present study. The correlation coefficients of 0.433, 0.866, and 0.369 for BW with BL, CG, and WH were observed in the present study. The best prediction of BW using two predictors (BL and GC) was BW =-56.522 + 0.509BL + 0.843CG, followed by using three predictors (BL, CG, and WH) was BW =-57.897+ 0.505BL + 0.839CG + 0.034WH, and using the only one predictor (CG) was BW =-28.443 + 0.905CG. The study revealed that CG and its combination with other linear body measurements can effectively define the body weight in Batur sheep. However, the highest R2 of 0.782 was observed when CG and BL were used as predictors.

Factors Affecting NPAs in Indian Banking Sector

Paradigm ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 181-193
Author(s):  
Nitya Garg
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Banking Sector ◽  
Relative Weight ◽  
Multiple Regression Model ◽  
Factors Affecting ◽  
Weight Analysis ◽  
Specific Factors ◽  
Relative Weight Analysis ◽  
Bank Specific Factors

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.

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