| Background Required Attendees should have a reasonable background in applied statistics. Beyond the basic concepts of measures of variation, covariation, natural logarithms, and subscripted variables, people need a decent understanding of least squares “regression” (e.g., β values, residual variance, residual sum of squares, R-sq, residuals), goodness-of-fit issues, sampling applications, and simple experimental designs. Ideally, attendees would have some experience with Fisher’s likelihood (e.g., logistic regression) concepts such as the likelihood function, log-likelihood function, maximum likelihood estimation, deviance, profile likelihood intervals) would be nice. (I understand that people may have little knowledge of likelihood issues). Participants are encouraged to attend the pre-workshop review on September 26th at UMSL. Disclaimer This is not a modeling course. Dr . Anderson will showcase a variety of models and attendees will gain some insights into modeling, but the short course does not focus on model building or the estimation of model parameters (via maximum likelihood or least squares). In addition, little is said about various random effects models. |
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Required background and disclaimer |