When used inappropriately, statistical models may give rise to misleading conclusions. Checking that a given model is an appropriate representation of the data is therefore an important step.

A second course in statistics with an emphasis on data analysis with applications in the social sciences. Students will gain hands on experience using R-- a programming language and software ...

For more than half a century, data analysts have used graphs to represent statistical models. In particular, graphical "conditional independence" models have emerged as a useful class of models.

Statistical modeling lies at the heart of data science. Well-crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In ...

Generalized Linear Models (GLMs) and Categorical Data Analysis (CDA) Course Topics Generally speaking, there are two types of outcomes (i.e. response) in statistical analysis: continuous and ...

The Programme for International Student Assessment comparative study of reading performance among 15-year-olds is reanalyzed using statistical procedures that allow the full complexity of the data ...

Data modeling is the framework that lets data analysis use data for decision-making. A combined approach is needed to maximize data insights.

For each indicator, the latest figure and its one-year, five-year, and 10-year changes are easy to understand in terms of raw data, but we need supplementary statistical analysis to determine ...