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Chained Equations

As seen in: The Startup, Wiley Online Library
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Articles

The Ecosystem of Middle School Math Achievement: Intersections of Student Demographic, Parent, and School Factors

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By Chained Equations, Garret J. Hall, Sabina Low, Keith Smolkowski
| Wiley Online Library Verified
Supporting Information Filename Description pits23345-sup-0001-Appendix.docx17.4 KB Supporting information. References , and . 2008. “Socioeconomic Differences in Reading Trajectories: The Contribution of Family, Neighborhood, and School Contexts.” Journal of educational psychology 100, no. 2: 235–251. https://doi.org/10.1037/0022-0663.100.2.235. , and . 2002. “Early Schooling and Social Stratification.” The transition to kindergarten: 13–38. , , and . 2003.
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MICE Algorithm to Impute Missing Values in a Dataset

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By Bhuvaneswari Gopalan, Jere Xu, Chained Equations, Kaili Chen
| The Startup Verified
Photo by Vilmos Heim on Unsplash Missing data is a common problem in math modeling and machine learning. Machine Learning models cannot inherently work with missing data, and hence it becomes imperative to learn how to properly decide between different kinds of imputation techniques to achieve the best possible model for the use case. Random forest (RF) missing data algorithms are an attractive approach for imputing missing data.
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