In a research on the entrepreneurial potential and business creation.pdf

•

0 likes•5 views

In a research on the entrepreneurial potential and business creation intentions of CEGEP students and graduates, we obtained the cross table Next concerning the distribution of entrepreneurial potential (obtained using an assessment test) according to the parents' sector of work. The theoretical distribution of respondents is also given according to the assumption that the two characters are independent. A) Find the value of the theoretical frequencies for I) the box (2,2), combination of Private-Medium modalities. Ii) the box (3,3), combination of the modalities On their own account B) Are the conditions for the application of the two-character independence test met? Explain. C) We want to determine the disparity between the observed distribution and the theoretical distribution of absolute frequencies under the assumption of independence. \begin{tabular}{|l|ccc|c|} \cline { 2 - 4 } \multicolumn{1}{c|}{} & \multicolumn{3}{c|}{ Rpartition observe } \\ \hline \multirow{2}{*}{Secteurdetravaildesparents} & \multicolumn{2}{c|}{ Potentiel entrepreneurial } & \multicolumn{1}{c}{} \\ \cline { 2 - 4 } \multicolumn{1}{c|}{} & Faible & Moyen & Fort & Total \\ \hline Public & 36 & 232 & 64 & 332 \\ Priv & 25 & 161 & 60 & 246 \\ A leur compte & 12 & 124 & 55 & 191 \\ \hline \multicolumn{1}{|c|}{ Total } & 73 & 517 & 179 & 769 \\ \hline \end{tabular}.

Education

Similar to In a research on the entrepreneurial potential and business creation.pdf

The growing population of elders in the society calls for a new approach in care giving. By inferring what activities elderly are performing in their houses it is possible to determine their physical and cognitive capabilities. In this paper we show the potential of important discriminative classifiers namely the Soft-Support Vector Machines (C-SVM), Conditional Random Fields (CRF) and k-Nearest Neighbors (k-NN) for recognizing activities from sensor patterns in a smart home environment. We address also the class imbalance problem in activity recognition field which has been known to hinder the learning performance of classifiers. Cost sensitive learning is attractive under most imbalanced circumstances, but it is difficult to determine the precise misclassification costs in practice. We introduce a new criterion for selecting the suitable cost parameter C of the C-SVM method. Through our evaluation on four real world imbalanced activity datasets, we demonstrate that C-SVM based on our proposed criterion outperforms the state-of-the-art discriminative methods in activity recognition.

IMPROVING SUPERVISED CLASSIFICATION OF DAILY ACTIVITIES LIVING USING NEW COST...

csandit

IRJET- Segmentation and Representation of Data Dependent Label Distribution L...

IRJET Journal

IMPROVING SUPERVISED CLASSIFICATION OF DAILY ACTIVITIES LIVING USING NEW COST...

cscpconf

IRJET- Analysis of Chi-Square Independence Test for Naïve Bayes Feature Selec...

IRJET Journal

This paper presents a knowledge based advisor system designed to aid preparatory year students to achieve their desire of colleges they wanted to be dwelled allocated. The system predicts the 2nd term GPA, and the final term GPA by knowing the 1st semester GPA, then advise students to suitable colleges. A student who is far from achieving his desire is advised to study some aided courses. The system uses historical data extracted from the university database. Then statistical prediction algorithms are developed, to evaluate and to match students’ current desires with colleges’ qualified criteria.

Students Knowledge based Advisor System for Colleges Admission With an Applie...

IJRES Journal

Data mining or Knowledge discovery (KDD) is extracting unknown (hidden) and useful knowledge from data. Data mining is widely used in many areas like retail, sales, ecommerce, remote sensing, bioinformatics etc. Student’s performance has become one of the most complex puzzle for universities and colleges in recent past with the tremendous growth. In this paper, authors deployed data mining techniques like classification, association rule, chi-square etc. for knowledge discovery. For this study, authors have used data set containing Approx. 180 MCA (post graduate) students results data of 3 colleges. Study found that one can apply data mining functionalities like Chi-square, Association rule and Lift in Education and discover areas of improvement.

RESULT MINING: ANALYSIS OF DATA MINING TECHNIQUES IN EDUCATION

International Journal of Technical Research & Application

IRJET- Effectiveness of Constructivist Instructional Approach on Achievem...

IRJET Journal

In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.

PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...

ijcax

Cs268

Ibnu Siroj

International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science. The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.

Analysis and Performance Prediction of Students Using Fuzzy Relations and Int...

International Journal of Engineering Inventions www.ijeijournal.com

Statistics-assignment_ldp_609_2014_22 (1)

Samwell Rutto

IRJET- Using Data Mining to Predict Students Performance

IRJET Journal

Data mining techniques (DMT) are extensively used in educational field to find new hidden patterns from student’s data. In recent years, the greatest issues that educational institutions are facing the unstable expansion of educational data and to utilize this information data to progress the quality of managerial decisions. Educational institutions are playing a prominent role in the public and also playing an essential role for enlargement and progress of nation. The idea is predicting the paths of students, thus identifying the student achievement. The data mining methods are very useful in predicting the educational database. Educational data mining is concerns with improving techniques for determining knowledge from data which comes from the educational database. However it has issue with accuracy of classification algorithms. To overcome this problem the higher accuracy of the classification J48 algorithm is used. This work takes consideration with the locality and the performance of the student in education in order to analyse the student achievement is high over schooling or in graduation

Empirical Study on Classification Algorithm For Evaluation of Students Academ...

iosrjce

K017626773

IOSR Journals

Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another issue that can be addressed from the results of this research since predicting student performance in advance will enable University management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses. Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers look forward to try and improve.

A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...

Editor IJCATR

A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...

Editor IJCATR

PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...

ijcax

PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...

ijcax

PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...

ijcax

Automated Question Paper Generator And Answer Checker Using Information Retri...

Sheila Sinclair

Similar to In a research on the entrepreneurial potential and business creation.pdf (20)