Research of Grey Incidence Cluster Prediction Analysis Model

2012 ◽  
Author(s):  
Li Dong ◽  
Kong Lifang ◽  
Zhao Ying
Keyword(s):  
Analysis Model ◽  
Prediction Analysis

Prediction analysis model of integrated carrying capacity using set pair analysis

2016 ◽  
Vol 120 ◽  
pp. 39-48 ◽  
Author(s):  
Chao Wei ◽  
Xiaoyan Dai ◽  
Shufeng Ye ◽  
Zhongyang Guo ◽  
Jianping Wu
Keyword(s):  
Carrying Capacity ◽  
Set Pair Analysis ◽  
Analysis Model ◽  
Prediction Analysis ◽  
Pair Analysis

Hybrid fuzzy logic with SVM based prediction analysis model to predict innovation performance of 3C Industry

2021 ◽  
pp. 1-8
Author(s):  
Chiu-Lan Chang ◽  
Ming Fang
Keyword(s):  
Fuzzy Logic ◽  
Statistical Technique ◽  
Innovation Performance ◽  
Support Vector ◽  
Top Management ◽  
Management Innovation ◽  
Analysis Model ◽  
Financial Flexibility ◽  
Prediction Analysis ◽  
Econometric Approach

This paper explores the applications of support vector machines (SVM) technique and panel data econometric approach in innovation performance. We proposed Hybrid Fuzzy Logic with SVM based prediction analysis model to predict Innovation Performance of 3C Industry and then we construct the top management innovation awareness by text mining analysis, which differs from traditional methods, and then discusses the mediation function of financial flexibility in the relationship between innovation awareness and enterprise performance. Then, we use the financial econometric method panel regression and apply the SVM, a statistical technique that has gained special popularity in the field of AI to test the relation between innovation performance and innovation awareness of top management in 3C industry. The findings show that there exists a significant position relationship between innovation awareness and innovation performance, and that the mediation function of financial flexibility does work. With the SVM approach, the innovation performance can be predicted well by top management innovation awareness.

Application of MATLAB-Based Regression Analysis Model in Enterprises

2013 ◽  
Vol 328 ◽  
pp. 239-243 ◽  
Author(s):  
Ling Jiang
Keyword(s):  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Economic Benefits ◽  
Product Line ◽  
Analysis Model ◽  
Prediction Analysis ◽  
Business Operation ◽  
Product Sales ◽  
Prediction And Forecasting ◽  
Product Output

Prediction analysis is the prerequisite and basis of decision-making. Regression analysis is one of quantitative forecasting methods for prediction and forecasting of the variable change based on causality. Regression analysis can be adopted to build mathematical models for prediction analysis for the product output and cost, the product sales, the economic benefits of every product line, etc. This paper studies the enterprises production and business operation activities by using linear regression analysis method, with some practical examples to illustrate the solution with application of MATLAB software.

scholarly journals A Data Mining Approach for Prediction Modeling Using Association rule

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Sarkhel H.Taher Karim ◽  
Rzgar Sirwan Raza
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Optimization Problems ◽  
Analysis Model ◽  
Prediction Modeling ◽  
Prediction Analysis ◽  
Data Mining Approach

   In the present work, a data mining approach is highlighted, a prediction optimization data mining approach association rule is chosen for performing prediction modeling in a supermarket application, a data mining prediction analysis model is formulated based on association rule is presented in this work.  The result of the model formulated is then compared with the result produced on the similar set of input on the traditional optimization problems. While comparing the results it was observed that the result produced by the presented model is much closer to the reality.

An Input–Process–Output Model of Pilot Core Competencies

2017 ◽  
Vol 7 (2) ◽  
pp. 78-85 ◽  
Author(s):  
Heikki Mansikka ◽  
Don Harris ◽  
Kai Virtanen
Keyword(s):  
Path Analysis ◽  
Team Performance ◽  
Core Competencies ◽  
Core Competency ◽  
Principal Component ◽  
Analysis Model ◽  
Input Process ◽  
The Core ◽  
Performance Framework ◽  
Process Output

Abstract. The aim of this study was to investigate the relationship between the flight-related core competencies for professional airline pilots and to structuralize them as components in a team performance framework. To achieve this, the core competency scores from a total of 2,560 OPC (Operator Proficiency Check) missions were analyzed. A principal component analysis (PCA) of pilots’ performance scores across the different competencies was conducted. Four principal components were extracted and a path analysis model was constructed on the basis of these factors. The path analysis utilizing the core competencies extracted adopted an input–process–output’ (IPO) model of team performance related directly to the activities on the flight deck. The results of the PCA and the path analysis strongly supported the proposed IPO model.

Is That Measure Really One-Dimensional?

Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 188-196 ◽  
Author(s):  
Esther T. Beierl ◽  
Markus Bühner ◽  
Moritz Heene
Keyword(s):  
Model Fit ◽  
Nuisance Parameters ◽  
Factorial Validity ◽  
Fit Indices ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
One Dimensional ◽  
Confirmatory Factor Analysis Model ◽  
Cutoff Values ◽  
Confirmatory Factor

Abstract. Factorial validity is often assessed using confirmatory factor analysis. Model fit is commonly evaluated using the cutoff values for the fit indices proposed by Hu and Bentler (1999) . There is a body of research showing that those cutoff values cannot be generalized. Model fit does not only depend on the severity of misspecification, but also on nuisance parameters, which are independent of the misspecification. Using a simulation study, we demonstrate their influence on measures of model fit. We specified a severe misspecification, omitting a second factor, which signifies factorial invalidity. Measures of model fit showed only small misfit because nuisance parameters, magnitude of factor loadings and a balanced/imbalanced number of indicators per factor, also influenced the degree of misfit. Drawing from our results, we discuss challenges in the assessment of factorial validity.

Pengaruh Faktor Fundamental, Risiko Sistematis Dan Ekonomi Makro Terhadap Return Saham Syariah Yang Tergabung Di Jakarta Islamic Index (JII) Pada Periode 2010 – 2014

2017 ◽  
Vol 24 (1) ◽  
pp. 54-70
Author(s):  
Hasanah Setyowati ◽  
Riyanti Ningsih
Keyword(s):  
Exchange Rate ◽  
Stock Exchange ◽  
Systematic Risk ◽  
Secondary Data ◽  
Analysis Model ◽  
Macroeconomic Factors ◽  
Return On Equity ◽  
Equity Ratio ◽  
Regression Techniques ◽  
The Exchange Rate

This study aimed to obtain empirical evidence on the influence of fundamental factors, systematic risk and macroeconomics on the returns Islamic stock of companies incorporated in the Jakarta Islamic Index in 2010-2014. The variables used were the fundamental factors that are proxied by Earning Per Share (EPS), Return on Equity (ROE), Debt to Equity Ratio (DER); Systematic risk is proxied by Beta Shares; macroeconomic factors is proxied by the inflation rate and the exchange rate. The samples of this study are the enterprises incorporated in Jakarta Islamic Index (JII) at the Indonesian Stock Exchange. The sampling method was using purposive sampling. There were 12 samples of Islamic stocks that meet the criteria to be used as samples. The analysis model used is multiple linear regression techniques and the type of data used is secondary data. The study found that all variables, which are Earning Per Share (EPS), Return on Equity (ROE), Debt to Equity Ratio (DER), Beta stock, inflation and the exchange rate do not significantly affect the return of sharia stock either simultaneously or partially.

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