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 ...

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, ...

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 ...

Enhance your skills in energy statistical analysis with our expanded three-day workshop. Dive deep into crucial topics like risk quantification and pricing models using practical exercises in Excel.

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

Scandinavian Journal of Statistics, Vol. 32, No. 2 (Jun., 2005), pp. 223-240 (18 pages) We review and extend some statistical tools that have proved useful for analysing functional data. Functional ...

Conclusions Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving ...