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.

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 ...

Data analysis and graphics using R: an example-based approach. Cambridge University Press Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge ...

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

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, ...

In today's data-driven world, statistical analysis plays a critical role in uncovering insights, validating hypotheses, and driving decision-making across industries. R, a powerful programming ...

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

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 ...