Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

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 modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...

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 ...

Offered through an interdisciplinary partnership, data science at CU Boulder is delivered by the Departments of Applied Mathematics, Computer Science, and Information Science and awarded by the ...

When you use the statistical analysis features in Excel, you are leveraging one of the most powerful tools available for data manipulation and interpretation. Excel is not just a spreadsheet ...

A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...

How to use statistical tools for component tolerance analysis. A look at methods such as Monte Carlo and Gaussian distribution. Simulating a dc-dc converter in LTspice to model closed-loop voltage ...