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 ...

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.

It will cover an introduction to quantitative research design and causal inference, exploratory data analysis and data visualisation, generalised linear models, and generalised latent variable models ...

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

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 ...

Multiple-bias models provide alternatives that allow one systematically to integrate major sources of uncertainty, and thus to provide better input to research planning and policy analysis. Typically, ...

The Center for Data Analysis specializes in quantitative research and simulation modeling of public policies for the Heritage Foundation. The CDA specializes in modeling the effects of federal ...

Learning how to perform statistical data analysis in Microsoft Excel can help you improve your analytics and reporting. This guide takes you ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...