The act of lending is based on trust in the borrower to honour the obligation of paying back the lender. Greater spreads on credit operations may help predict the expected recovery of the credit, based on the sufficiency and liquidity of the guarantee. This study aims to understand how predictive models can provide different estimations of expected recovery based on the same data sets. It classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. It finds that a simple logistic regression model can easily be extended to a multiple logistic regression model by integrating more than one prediction variable, which indicates increasing difficulty in obtaining multiple observations with an increasing number of independent variables. It compares the efficiency of the logistic regression with that of a linear regression in predicting whether recovery is due in a credit operation, and, thus, identifies the best model for this purpose.

中文翻译：

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基于线性和逻辑回归的信用操作决策支持系统

贷款行为是基于对借款人的信任，以履行偿还贷款人的义务。基于担保的充足性和流动性，信贷业务的更大利差可能有助于预测信贷的预期回收。本研究旨在了解预测模型如何基于相同的数据集提供不同的预期恢复估计。它通过制定规则来对信用进行分类，该规则根据某些指定的定义描述分类变量的值。发现通过整合多个预测变量，简单的逻辑回归模型可以很容易地扩展为多元逻辑回归模型，这表明随着自变量数量的增加，获得多个观测值的难度越来越大。