code

For many analyses, it is common to use both bootstrapping and multiple imputation (MI): MI to address missing data and bootstrapping to obtain standard errors. For example, when using the g-formula in causal inference, bootstrapping is required to obtain standard errors; however, the data may be multiply imputed due to missing (baseline) data in the population of interest.

Our introcutory book contains a comprehensive and thorough overview of fundamental statistical concepts such as hypothesis testing, inference and regression; and each chapter is accompanied by a wealth of exercises and solutions.