Statistical Modeling

Generalized linear models, Interpretation of coefficients from a causal inference perspective, Simple linear regression, Logistic regression, Poisson regression, Survival data analysis, Longitudinal data analysis, Model fit diagnosis, Residual study, Violations of basic assumptions , Model selection, Multicollinearity, Variable transformations, Hierarchical / multilevel models / random effects / fragility.

 

 

Basic Information

Workload
60 hours
Requirements
Statistical inference

Mandatory:

  • Fahrmeir, L. (2013). Regression: models, methods and applications. New York, Springer.
  • Gelman, A. and J. Hill (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge; New York, Cambridge University Press.
  • Congdon, P.D. (2020) Bayesian Hierarchical Models with Applications Using R Second Edition. Boca Raton, CRC Press/Taylor & Francis Group

Complementary:

  • Kline, R. B. (2016). Principles and practice of structural equation modeling. New York, Guilford Press.
  • McElreath, R. (2016). Statistical rethinking: a Bayesian course with examples in R and Stan. Boca Raton, CRC Press/Taylor & Francis Group.
  • Draper, N.R. & Smith, H., Applied Regression Analysis - Third Edition, 1998, John Wiley & Sons
  • Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. New York: Springer series in statistics, 2001.
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