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A linear regression model for math perfomance of italian high school students

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Math performance, a linear regression model

Project Part of the Course METHODS AND MODELS OF STATISTICAL INFERENCE (A.Y. 2023/2024)

Authors: Maria Chiara Menicucci, Leonardo Pascotto, Alessandro Pedone, Arianna Perotti

Content:

  • LaTeX presentation
  • dataset with code for creating the .txt file
  • code for data analysis

Dataset: OECD PISA 2022 survey data available on the OECD website

Tools Used:

  • data visualization
  • non-parametric ANOVA (Kruskal-Wallis and Dunn's test)
  • removal of influential points (outliers and leverages)
  • variable selection (stepwise backward and forward selection with BIC and AIC)
  • model validation (homoscedasticity, normality test)
  • VIF (Variance Inflation Factor) and GVIF (generalized VIF)
  • cross-validation with K-fold and leave-one-out
  • prediction and confidence intervals

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A linear regression model for math perfomance of italian high school students

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  • R 53.6%
  • TeX 46.4%