Measures of Central Tendency

This study aimed to determine the entrepreneurial engagement of Agri-Business graduates from Southern Philippines Agri-Business and Marine and Aquatic School of Technology (SPAMAST), during the SY 2008-2013. The data was collected using a self-administered questionnaire, analyzed and subjected to the measures of central tendency (mean and percentage) and the Statistical Package for Social Sciences (SPSS 19.0).Results showed that graduates were within 26 to 30 years old age, female, single, most were regular workers in a private company with 1 - 3 years working experience and were practicing entrepreneurs earning a monthly income of 10,000. High rating was extended to the level of competence on attitudinal, behavioral and educational factors. It was found out that, the respondents either felt, thought and view entrepreneurship as a thing that they had dreamed to undertake after graduation because they believed that it is only doing entrepreneurial undertakings that they can fulfill the objectives of the course and their personal beliefs that success can be attained through it.Further, only few graduates had started their entrepreneurial engagement, while the majority, were still thinking about their entrepreneurial endeavor because of the difficulty in starting own business due to the complex administrative procedures involved. The demographic and socio-economic profile had no significant influence to the level of engagement while the level of competencies significantly influenced the level of entrepreneurial engagement.

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Central Tendency Bias and Self-Reported Risk Attitudes

SSRN Electronic Journal ◽

10.2139/ssrn.2050899 ◽

2012 ◽

Cited By ~ 3

Author(s):

Alexander Klos

Keyword(s):

Risk Attitudes ◽

Central Tendency ◽

Central Tendency Bias

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Learning design using PMRI to teach central tendency materials

Journal of Physics Conference Series ◽

10.1088/1742-6596/1470/1/012086 ◽

2020 ◽

Vol 1470 ◽

pp. 012086

Author(s):

A Meitrilova ◽

R I I Putri

Keyword(s):

Learning Design ◽

Central Tendency

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A Multicriteria Goal Programming Model for Ranking Universities

Mathematics ◽

10.3390/math9050459 ◽

2021 ◽

Vol 9 (5) ◽

pp. 459

Author(s):

Fernando García ◽

Francisco Guijarro ◽

Javier Oliver

Keyword(s):

Goal Programming ◽

Programming Model ◽

Decision Makers ◽

University Rankings ◽

Central Tendency ◽

Performance Variables ◽

World University Rankings ◽

Goal Programming Model ◽

Objective Quantification

This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.

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Ensemble classification technique for heart disease prediction with meta-heuristic-enabled training system

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2020-0033 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Parvathaneni Rajendra Kumar ◽

Suban Ravichandran ◽

Satyala Narayana

Keyword(s):

Heart Disease ◽

Dimensionality Reduction ◽

Research Work ◽

Feature Reduction ◽

Ensemble Classification ◽

Support Vector ◽

Disease Prediction ◽

Central Tendency ◽

Sea Lion ◽

Qualitative Variation

AbstractObjectivesThis research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification.MethodsAs the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with Canberra Distance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)-based feature reduction approach is deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier.ResultsAn elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.ConclusionsFrom the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively.ResultsFinally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.

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