The presence of even a single outlier in a sample estimate can have strong repercussions on the regression models obtained with the method of least squares, nullifying its reliability. This is a condition to avoid in real estate appraisal where regression is used with predictive and explanatory purposes, and therefore it is essential that the regression model best represents the phenomenon investigated. In this study the outliers detection was carried out with a robust regression that uses the method of least median of squared residuals LMS. With the aid of a special software, the calculations were performed on a sample of houses recently sold in a district of the city of Bari Italy.

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We'd like to understand how you use our websites in order to improve them. Register your interest. In the context of growing relevance of social and ecological responsibility role in businesses, the sustainability trends inevitably change the course of development and the content of marketing decisions.

This article argues that the creation and maintenance of relationships with green consumers as well as increase in their loyalty are extremely important and can guarantee long-term competitive advantage for the company. Thus, the article aims to design a conceptual framework for the development of long-term relationships with green consumers and to reason it empirically with reference to the example of Lithuanian consumers of eco-textile products. The results of quantitative research questionnaire survey distinguish the main drivers influencing the relationships with consumers of eco-textile products and reveal the main components of customer loyalty.

The strength and significance of correlations among the factors that influence the relationships with eco-textile product consumers, the antecedents of long-term relationships, and consumer loyalty are analyzed. This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Ang, L. Customer retention management process: A quantitative study.

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LMS for Outliers Detection in the Analysis of a Real Estate Segment of Bari




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