McShane, Blakeley B.; Bradlow, Eric T.; Lynch Jr., John G.; Meyer, Robert J. "Statistical Significance" and Statistical Reporting: Moving Beyond Binary. Journal of ...

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 ...

Various statistical forecasting methods exist designed for use with slow-moving products, new product introductions, stable mature products and products with erratic demand. Determining which ...

The federal statistical system is facing “unprecedented strain, uncertainty and transformation” in the face of staffing losses, funding pressures and threats to statistical integrity, according to a ...

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 ...

The 'Statement on Statistical Significance and P-Values' includes six principles underlying the proper use and interpretation of the p-value and is intended to improve the conduct and interpretation ...

Current student Rohan Rajiv is blogging once a week about important lessons he is learning at Kellogg. Read more of his posts here. In my last MBA learning post, we dove into the idea of managing ...

Statistical significance is a critical concept in data analysis and research. In essence, it’s a measure that allows researchers to assess whether the results of an experiment or study are due to ...

The Statistical & Data Sciences (SDS) Program links faculty and students from across the college interested in learning things from data. At Smith, students learn statistics by doing—class time ...

There are many different approaches for assessing process parameter criticality, and assessing which process parameters have a significant impact on critical quality attributes (CQAs) is a particular ...