Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

Academic Performance ◽

Linear Regression ◽

Principal Component ◽

Component Analysis

Groundwater quality model through the combined use of principal component analysis (PCA) and multiple linear regression (MLR) Case study: Maturín’s aquifers, Monagas, Venezuela

INNOTEC ◽

10.26461/20.02 ◽

2020 ◽

Keyword(s):

Linear Regression ◽

Groundwater Quality ◽

Principal Component ◽

Component Analysis ◽

Quality Model ◽

Combined Use

Development of a model to reproduce observed suspended sediment distributions in the southern North Sea using Principal Component Analysis and Multiple Linear Regression

Continental Shelf Research ◽

10.1016/s0278-4343(96)00057-x ◽

1997 ◽

Vol 17 (7) ◽

pp. 761-778 ◽

Cited By ~ 32

Author(s):

Julia P. McManus ◽

David Prandle

Keyword(s):

Linear Regression ◽

Suspended Sediment ◽

North Sea ◽

Principal Component ◽

Component Analysis ◽

Southern North Sea

Prediction of the processing factor for pesticides in apple juice by principal component analysis and multiple linear regression

Food Additives & Contaminants Part A ◽

10.1080/19440049.2012.749541 ◽

2013 ◽

Vol 30 (3) ◽

pp. 466-476 ◽

Cited By ~ 11

Author(s):

L. Martin ◽

M. Mezcua ◽

C. Ferrer ◽

M.D. Gil Garcia ◽

O. Malato ◽

...

Keyword(s):

Linear Regression ◽

Apple Juice ◽

Principal Component ◽

Component Analysis ◽

Processing Factor

Principal determinants of toxicity reduction by de-oiled soya using multivariate statistics: principal component analysis and multiple linear regression analysis

Applied Ecology and Environmental Research ◽

10.15666/aeer/1503_17171737 ◽

2017 ◽

Vol 15 (3) ◽

pp. 1717-1737 ◽

Cited By ~ 3

Author(s):

S Suhajta

Keyword(s):

Regression Analysis ◽

Linear Regression ◽

Multivariate Statistics ◽

Linear Regression Analysis ◽

Principal Component ◽

Component Analysis ◽

Multiple Linear Regression Analysis ◽

Toxicity Reduction

Research on the impact of the digital economy on the residents’ lives with high quality based on principal component analysis and multiple linear regression methods

10.1109/dsins54396.2021.9670586 ◽

2021 ◽

Author(s):

Aichen Ni

Keyword(s):

Linear Regression ◽

Principal Component ◽

Component Analysis ◽

Digital Economy ◽

High Quality ◽

Regression Methods ◽

The Impact

2012 6th International Conference on Application of Information and Communication Technologies (AICT) ◽

Comparison of the efficiency of principal component analysis and multiple linear regression to determine students' academic achievement

10.1109/icaict.2012.6398537 ◽

2012 ◽

Author(s):

Mehtap Erguven

Keyword(s):

Academic Achievement ◽

Linear Regression ◽

Principal Component ◽

Component Analysis

Application of Multiple Linear Regression and Extended Principal-Component Analysis to Determination of the Acid Dissociation Constant of 7-Hydroxycoumarin in Water/AOT/Isooctane Reverse Micelles

Journal of Colloid and Interface Science ◽

10.1006/jcis.1999.6584 ◽

2000 ◽

Vol 221 (2) ◽

pp. 173-180 ◽

Cited By ~ 3

Author(s):

M. Caselli ◽

V. Daniele ◽

A. Mangone ◽

P. Paolillo

Keyword(s):

Linear Regression ◽

Dissociation Constant ◽

Reverse Micelles ◽

Principal Component ◽

Component Analysis ◽

Acid Dissociation ◽

Acid Dissociation Constant

2018 IEEE 4th International Symposium in Robotics and Manufacturing Automation (ROMA) ◽

Application of Principal Component Analysis vs. Multiple Linear Regression in Resolving Influential Factor Subject to Air Booster Compressor Motor Failure

10.1109/roma46407.2018.8986722 ◽

2018 ◽

Author(s):

Nurfatihah Syalwiah Binti Rosli ◽

Rosdiazli Bin Ibrahim ◽

Idris Ismail ◽

Kishore Bingi

Keyword(s):

Linear Regression ◽

Principal Component ◽

Component Analysis ◽

Influential Factor ◽

Booster Compressor ◽

Motor Failure

Principal component analysis (PCA) and multiple linear regression (MLR) statistical tools to evaluate the effect of E-beam irradiation on ready-to-eat food

Journal of Food Composition and Analysis ◽

10.1016/j.jfca.2010.11.010 ◽

2011 ◽

Vol 24 (3) ◽

pp. 456-464 ◽

Cited By ~ 45

Author(s):

Vanesa Guillén-Casla ◽

Noelia Rosales-Conrado ◽

María Eugenia León-González ◽

Luis Vicente Pérez-Arribas ◽

Luis María Polo-Díez

Keyword(s):

Linear Regression ◽

Principal Component ◽

Component Analysis ◽

Beam Irradiation ◽

Statistical Tools

Genetic Variability, Standardized Multiple Linear Regression and Principal Component Analysis to Determine Some Important Sesame Yield Components

AGRIVITA Journal of Agricultural Science ◽

10.17503/agrivita.v39i1.843 ◽

2017 ◽

Vol 39 (1) ◽

Author(s):

Vina Eka Aristya ◽

Taryono Taryono ◽

Rani Agustina Wulandari

Keyword(s):

Linear Regression ◽

Genetic Variability ◽

Yield Components ◽

Principal Component ◽

Component Analysis