This workshop will include a comparison of least-squares, maximum likelihood, and minimum variance quadratic unbiased methods of estimation. The assumptions used to make inferences in ordinary linear regression are that the residuals are independent of each other and are normally distributed with a mean of zero and constant variance. Violations of these assumptions could cause the results to be misleading. In this workshop, you will learn how to assess the goodness of fit of a regression model with diagnostic and influence plots. Also included will be added variable and partial residual plots, which are helpful in determining whether additional predictors are useful in a multiple regression model.
Most researchers use graphics to illustrate the results of a statistical analysis.
In this workshop we will explore the use of graphics and visualization as analysis tools in their own right. These graphical methods can be used to describe and compare distributions, examine associations among variables, provide a representation of several variables at once, and support statistical inference. When using several predictors to model a response in ordinary linear regression, the model may be difficult to interpret if those predictors are correlated with each other. A predictor known to have a positive effect may have a negative coefficient, or a strong predictor may not have a significant coefficient. In this workshop, the problem of multicollinearity will be discussed, and various statistics used to measure multicollinearity will be described.