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Использование бинарной регрессии для оценки кандидатов при приеме на работу

Использование бинарной регрессии для оценки кандидатов при приеме на работу

Abstract

The paper shows the possibility of the efficient evaluation of candidates for positions with the help of the binary-regression. The absence of expertise in using math methods by personnel departments makes recruitment process modeling inefficient, so the results obtained via binary-regression is of great importance. The purpose of the research is to show the relationship between the data in CVs and the fact of passing the probation period by employees. The author had at his disposal data of candidates’ CVs provided by several HR-agencies to their clients. Some of employees had passed the probation, some of them had not passed

В статье исследована возможность оценки кандидатов на вакантные должности с использова нием бинарной регрессии. Ценность результатов обусловлена отсутствием на практике каких-либо средств автоматизации принятия решений в данной сфере. Цель исследования показать наличие статистической зависимости между информацией, указанной в резюме работника, и результатом прохождения испытательного срока. В качестве исследуемых данных были взяты резюме соискателей, рекомендованных несколькими кадро выми агентствами, в том числе резюме принятых на работу и резюме не прошедших испытательный срок.

Keywords

ПОДБОР ПЕРСОНАЛА, БИНАРНАЯ РЕГРЕССИЯ, ПРОБИТ-МОДЕЛЬ

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average