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 ...

The following is a summary of a story that originally appears on the Trinity College of Arts & Sciences website. As a new assistant professor in Duke’s Department of Statistical Science, Lasse ...

Looking to get into statistical programming but lack industry experience? We spoke with several statistical programmers from diverse backgrounds, and one thing became clear—there’s no single path to ...

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 ...

Solve Real-World Problems With Applied Statistics. Applied Statistics is the implementation of statistical methods, techniques, and theories to real-world problems and situations in several fields, ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Predictive analysis refers to the use of historical data and analyzing it using statistics to predict future events. It takes place in seven steps, and these are: defining the project, data collection ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...