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Model regresji logistycznej w badaniu popytu na ubezpieczenia życiowe

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Dembowska B., Worach-Kardas H.
Wiadomości Statystyczne. The Polish Statistician
|
2004
|
issue nr 10
1-9
XX
Zwrócono uwagę na konieczność podjęcia badań na poziomie mikroekonomicznym w celu rozpoznania uwarunkowań zachowań konsumentów kształtujących popyt na usługi ubezpieczeniowe. Celem opracowania jest wyodrębnienie określonych uwarunkowań demograficznych i socjoekonomicznych popytu na ubezpieczenia życiowe w populacji studentów województwa łódzkiego. Badanie przeprowadzono w styczniu 2003 r. wśród studentów studiów zaocznych i wieczorowych, w czterech niepublicznych uczelniach działających w województwie łódzkim.
EN
The target of the article is to select proper demographic and socio-economic conditions of demand on life insurance in population of students of the Łódzkie voivodship. The survey was conducted in January 2003 among extramural students of non-public universities. In the elaboration for identification of impact of particular variables on demand on life insurance a model of logistic regression was applied. Two-step survey procedure was used. The first step was connected with single parameter, where identified if particular variables: sex, age, civil status, place of living, children, source of maintenance, material status of household can decide about inclination for contracting a life insurance. The second step was connected with variables, which become statistically essential during the first step and was concerned surveying its joint impact. Applied survey method presented that the following variables: age under 29 years, good material situation of household and children decreased probability of contracting a life insurance. Then, the following elements: sex- woman, being in a marriage, living in the city, employment in the public and private sector or running an economic activity will stimulate for demanding on life insurance i.e. increase a chance to contract a life insurance. Received survey results confirm usability of logistic regression model to analyse conditions of demand on life insurance. (original abstract)
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