Here we present a sample workflow for analyzing assay-endpoint readouts from high-throughput RNAi screening data with guidance on choice and application of relevant statistical methods.

This course covers most frequently used statistical methods for data analysis. In addition to the standard inference methods such as parameter estimation, hypothesis testing, linear models and ...

Historically, statistical methods have not been employed to full advantage in plant paleoecology. This is in part because some of the most useful methods can be found only in the technical statistical ...

MIST.7370 — Graduate Id: 038569 Offering: 1 Credits: 3-3 Description This course introduces statistical methods and techniques for multivariate data analysis. The course studies basic ideas underlying ...

Ke-Hai Yuan, Peter M. Bentler, Three Likelihood-Based Methods for Mean and Covariance Structure Analysis with Nonnormal Missing Data, Sociological Methodology, Vol. 30 (2000), pp. 165-200 ...

Bachelor of Arts in Statistics & Data Science Our students become well-rounded critical thinkers who understand statistical methods and cutting-edge data analysis, and who go on to enjoy careers as ...

The assumption that analysis methods can compensate for such missing data are not justified, so aspects of trial design that limit the likelihood of missing data should be an important objective.