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 […]
Response surface methodology is a type of experimental design in which the goal is to determine settings of inputs or predictor variables for optimizing the expected response. Topics covered include: First and Second-order models response surface and contour plots central composite designs Box-Behnken designs The designs and methodology will be illustrated using examples from […]
This workshop will teach participants how to make sense of their output from linear statistical models, with an emphasis on the meaning of the model parameters. The workshop is intended for participants who have at least one semester of statistics and some previous experience with linear regression or ANOVA. This is a review and is […]