A study on the Performance Evaluation of Private Colleges Using the Multi-Regression Analysis and DEA Combination Model

2019 ◽  
Vol 19 (24) ◽  
pp. 359-383
Author(s):  
ByungHwa Lee ◽  
YoungJin Moon
Keyword(s):  
Regression Analysis ◽  
Performance Evaluation ◽  
Private Colleges ◽  
Combination Model

A nonlinear regression analysis method based on Stakeholder Theory Model for performance evaluation of poverty reduction of agricultural operators

2019 ◽  
pp. 002072091989420
Author(s):  
Zhenmin Wang
Keyword(s):  
Regression Analysis ◽  
Performance Evaluation ◽  
Stakeholder Theory ◽  
Nonlinear Regression ◽  
Poverty Reduction ◽  
Least Squares Method ◽  
Theory Model ◽  
Analysis Method ◽  
Nonlinear Regression Analysis ◽  
Policy Supply

A nonlinear regression analysis method for performance evaluation of poverty reduction of agricultural operators based on Stakeholder Theory Model analysis was proposed to improve the effectiveness of performance evaluation of poverty reduction of a new type agricultural operator in poverty-stricken areas. Firstly, it can find out which interest demands of different stakeholders and which interest realization paths have significant impact on the task performance and peripheral performance of the policy supply of new agricultural operators through factor analysis and regression analysis of the questionnaire data of stakeholders to provide sufficient basis for the appropriate revision and adjustment of policy supply; secondly, the nonlinear regression model is established by using the nonlinear least squares method to fit, control, and forecast the performance evaluation of poverty reduction behavior of agricultural operators, which is more accurate than linear regression in both object and method. Finally, the effectiveness of the proposed algorithm is verified by empirical analysis.

scholarly journals Pengaruh Informasi Akuntansi Terhadap Kinerja Manajerial dengan Tiga Variabel Moderating (Studi Empiris Pada BPR di Kabupaten Bogor)

2011 ◽  
Vol 1 (2) ◽  
pp. 115
Author(s):  
Dona Primasari ◽  
Isbandriyati Mutmainah
Keyword(s):  
Regression Analysis ◽  
Performance Evaluation ◽  
Business Strategy ◽  
Accounting Information ◽  
Least Square ◽  
Managerial Performance ◽  
Variable Environment ◽  
Least Square Regression ◽  
Task Uncertainty ◽  
Manager Performance

This research is based on the importance of accounting information to the manager’s performance evaluation, which is moderated with environment uncertainty, task uncertainty, and business strategy. It was used the population of Bank Perkreditan Rakyat on Kabupaten Banyumas. The influence of accounting information to managerial performance was analyzed by using original least square regression, and the influence of three moderated variable (environment uncertainty, task uncertainty, and business strategy) to manager performance were analyzed by using Moderated Regression Analysis (MRA). The analysis result showed that accounting information variable influenced manager performance significantly. Meanwhile, the variables of environment uncertainty, task uncertainty, and business strategy did not moderate the influence of accounting information to manager performance.In other words, those three variables could not be said as moderated variables. The result showed indication of the importance of accounting information in doing management’s function Keywords: accounting information, the manager’s performance evaluation, environment uncertainty, task uncertainty, and business strategy.

scholarly journals Determination of Infiltration Model Parameters Using Basic Soil Physical Properties

2021 ◽  
Author(s):  
Tabasum Rasool ◽  
Abdul Qayoom Dar ◽  
Mushtaq Ahmad Wani
Keyword(s):  
Regression Analysis ◽  
Performance Evaluation ◽  
Soil Properties ◽  
Stepwise Regression ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Predictive Equations ◽  
Infiltration Model ◽  
Infiltration Models

Abstract Quantification of infiltration rate is a time-consuming process because of its variability and challenges in the accurate estimation of infiltration model parameters. In this study predictive equations for parameters of Horton, Kostiakov, Modified Kostiakov and Philip infiltration models were developed using basic soil-properties. The model-parameters were initially determined applying non-linear Levenberg Marquardt algorithm (LMA) on field-observed infiltration data and were subsequently determined by predictive equations developed after applying regression analysis to investigated soil-properties. Regression analysis was carried-out using stepwise-regression (SR) where all the measured soil-properties were used, and by applying principal component analysis (PCA) prior to multiple linear-regression for reducing number of predictors. The results revealed that developed equations using stepwise regression and the ones developed after applying PCA were able to explain 40- 78% and 10- 50% of variation respectively. The performance evaluation of developed regression equations at two information levels along with LMA for prediction of infiltration model-parameters was carried out by computing an overall performance index (OPI), which combines relative weight of different statistical indicators, namely, Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (ENS), Willmott’s Index of Agreement (W), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Performance evaluation revealed, LMA with highest OPI-value is most suitable to ascertain parameters of studied infiltration models. However, for selected models using parameters determined at two information levels, it was observed that there exists no significant difference in OPI-value of computed infiltration rates suggesting that equations developed after PCA can be used successfully for determination of infiltration model-parameters.

Performance evaluation of multi-channel analyzers by use of linearly-related survey specimens.

1978 ◽  
Vol 24 (3) ◽  
pp. 403-413 ◽  
Author(s):  
T E Caragher ◽  
G F Grannis
Keyword(s):  
Regression Analysis ◽  
Performance Evaluation ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Specimen Preparation ◽  
Instrument Calibration ◽  
Analytical Results ◽  
Laboratory Survey

Abstract Sets of specimens having quantitative linear inter-relationships for 25 analytes were prepared and used in a small survey of results with multi-channel analyzers. Instrument calibration was evaluated by linear regression analysis of the analytical results, with calculation of the x- and y-intercepts and slopes. The average intercepts and slopes agreed quite well with those expected on the basis of specimen preparation, but the results from individual laboratories and from particular kinds of instruments demonstrated a variety of analytical biases. We conclude that performance of multi-channel analyzers can be evaluated effectively by such use of linearly related specimens in an inter-laboratory survey.

scholarly journals The application of big data in the performance evaluation of private colleges and Universities

2020 ◽  
Vol 214 ◽  
pp. 01025
Author(s):  
Yuan Quan ◽  
Junfeng Wang ◽  
Shuwen Li ◽  
Wang Dan
Keyword(s):  
Big Data ◽  
Performance Evaluation ◽  
Private Colleges ◽  
Work Performance ◽  
Rapid Development ◽  
Influencing Factor ◽  
Colleges And Universities ◽  
Long Time ◽  
University Performance ◽  
Private Colleges And Universities

The rapid development of big data has brought new opportunities for the operation and development of private colleges and universities. As an important way for private colleges and universities to assess teachers’ work, performance evaluation is not only an important guarantee for promoting teachers’ work, but also an important influencing factor for private colleges and universities to stand for a long time. There are some problems in the traditional way of university performance evaluation. This paper will further explore the specific application of big data in the performance evaluation of private universities based on its application characteristics.

scholarly journals Performance Evaluation of Regression Analysis Algorithms in Predicting the Enrollment of Basic Education

2022 ◽  
Vol 12 (1) ◽  
pp. 173-177
Author(s):  
Elizalde L. Piol ◽  
◽  
Luisito Lolong Lacatan ◽  
Jaime P. Pulumbarit
Keyword(s):  
Regression Analysis ◽  
Performance Evaluation ◽  
Linear Regression ◽  
Basic Education ◽  
Absolute Error ◽  
The Philippines ◽  
Fundamental Parameters ◽  
Percentage Points ◽  
Data Collecting ◽  
The Absolute

The use of Linear Regression in predicting enrolment has been shown to be beneficial, although it varies with various datasets and attributes; varying weights of the correlation of the attributes can be discarded if they do not impact the prediction. Data collecting had grown since prior investigations, resulting in a more complicated dataset with many varieties. As a result of the data being created by multiple clerks, cleaning and combining proved tough; nonetheless, the fundamental parameters remain intact. Different algorithms were examined but Linear Regression obtained the highest accuracy with a 12.398 percentage for the absolute error and a root mean squared of 26.936 to create a tangible model to anticipate the enrolment of Region IVA CALABARZON in the Philippines. This demonstrates that it was 2.067 percentage points more than the prior research.

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