Research Article
Analysis of the Influence and Prediction of the Number of Students on PNBP using Multiple Regression
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 174 - Issue 26 |
| Published: Mar 2021 |
| Authors: Alfrianus Papuas, Ella H. Israel, Noldy Sinsu |
10.5120/ijca2021921189
|
Alfrianus Papuas, Ella H. Israel, Noldy Sinsu . Analysis of the Influence and Prediction of the Number of Students on PNBP using Multiple Regression. International Journal of Computer Applications. 174, 26 (Mar 2021), 33-39. DOI=10.5120/ijca2021921189
@article{ 10.5120/ijca2021921189,
author = { Alfrianus Papuas,Ella H. Israel,Noldy Sinsu },
title = { Analysis of the Influence and Prediction of the Number of Students on PNBP using Multiple Regression },
journal = { International Journal of Computer Applications },
year = { 2021 },
volume = { 174 },
number = { 26 },
pages = { 33-39 },
doi = { 10.5120/ijca2021921189 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2021
%A Alfrianus Papuas
%A Ella H. Israel
%A Noldy Sinsu
%T Analysis of the Influence and Prediction of the Number of Students on PNBP using Multiple Regression%T
%J International Journal of Computer Applications
%V 174
%N 26
%P 33-39
%R 10.5120/ijca2021921189
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Conduct forecasting analysis with good accuracy for other PNBP components which can ultimately be the basis of PNBP receipt projection. Multiple Linear Regression is used to predict how the state (ups and downs) of independent variables, when two independent variables as predictor factors are manipulated (the ups and downs of values). By analyzing the relationship between several variables to measure the degree of relationship and the direction of the relationship between independent variables and dependent variables then predict a variable to forecast in the future. In this study simultaneously independent variables namely The Number of Student and Facilities influenced dependent variables namely PNBP and through multiple linear regression equations produced by future forecasting with the results of measurement of forecasting errors are very small.
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