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

45 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.