The Monte Carlo simulation technique, named for the famous Monaco gambling resort, originated during World War II as a way to model potential outcomes from a random chain of events. It is particularly ...

Explain why probability is important to statistics and data science. See the relationship between conditional and independent events in a statistical experiment. Calculate the expectation and variance ...

The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Build foundational knowledge of data science with this introduction ...

Topics covered include: basic concepts of probability theory and statistics, counting, axioms of probability, independence, Bayes rule, continuous and discrete random variables, moments, multiple ...

1. Introduction 2. Introduction to Probability Theory 3. Random Variables, Distributions and Density Functions 4. Operations on a Single Random Variable 5. Pairs of Random Variables 6. Multiple ...

The RANDOM statement defines the random effects constituting the vector in the mixed model. It can be used to specify traditional variance component models (as in the VARCOMP procedure) and to specify ...

Continuous variables are simulated using either Fleishman’s third order or Headrick’s fifth-order power method transformation. Simulation occurs at the component level for continuous mixture ...

Angelina Hammon, Sabine Zinn, Multiple imputation of binary multilevel missing not at random data, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 69, No. 3 (2020), pp.