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首页> 外文期刊>Data in Brief >Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset.
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Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset.

机译：Logit模型，接收器特征曲线的区域，敏感性和高校网络数据集共同注册密度的特异性。

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摘要

This article describes the data related to co-enrollment density (CD), a new network clustering index, that can predict persistence and graduation. The data hold the raw results and charts obtained with the algorithm for CD introduced in ``Co-Enrollment Density Predicts Engineering Students' Persistence and Graduation: College Networks and Logistic Regression Analysis.'' There are data for eight institutions that show CD as a predictor for graduation at four years, graduation at six years, and ever graduated. The files were processed usingRto estimate CD at one, two, three, and four years. Logistic regression models, receiver operating characteristic curves, specificity, sensitivity, and cut-off points were estimated for each model. TheRcode to reproduce the metanalysis for the summary data is included. The displays for the logistic regression models, receiver operating characteristic curves, density curves for classes, models, and parameters are included.
机译：本文介绍了与共登记密度（CD），新的网络聚类索引有关的数据，可以预测持久性和毕业。 数据持有使用“共同注册密度”预测工程学生的持久性和毕业中的CD算法获得的RAW结果和图表：大学网络和逻辑回归分析。''有八个机构的数据显示CD作为一个 四年毕业预测者，六年毕业，毕业。 使用该文件在一个，两个，三个和四年中使用估计CD进行处理。 估计每个模型的逻辑回归模型，接收器操作特征曲线，特异性，灵敏度和截止点。 包括将要重现摘要数据的元分析的Thercode。 包括逻辑回归模型，接收器操作特征曲线，类，模型和参数的密度曲线的显示器。

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