This workshop is designed for people who need to analyze their data using SAS. We will focus on the workflow, best practices, and procedures that any researcher should consider when faced with a new dataset and illustrate them in a hands-on fashion using SAS. Researchers should have access to a copy of SAS if they […]
Principal Component Analysis (PCA) is a popular multivariate technique used for data reduction. By analyzing the variance-covariance structure of a set of variables, uncorrelated linear combinations of the variables (called principal components) are calculated that maximize the amount of variance explained. The resulting principal components can then be used in further analyses such as regression […]
Multimodel inference (MMI) is a model selection framework that has recently gained some popularity as an alternative to null hypothesis significance testing. This type of inference favors stepwise approaches (forward and backwards model selection) to determine a single best “final” model. This workshop gives an overview of the MMI framework, in which researchers generate a […]
Ordination is a statistical technique that allows us to graphically summarize complex relationships. Also considered “dimension reduction,” these techniques offer a way to simplify many correlated variables (“dimensions”) into two or three, axes, which are must easier to interpret. In two dimensions we can then map our data points on a figure, and this often […]
Response surface methodology is a related design topic in which the goal is to determine settings for optimizing the expected response. The designs and methodology will be illustrated using examples from the rsm library in R.
This workshop is designed to teach researchers how to get started analyzing data.