Upon successful completion of this course, the student will be able to:
Understand derivation and distribution of linear and quadratic forms.
Understand definitions of non-central chi-square, t, and F distributions.
Derive Maximum Likelihood
Be able to derive maximum likelihood estimates of parameters in a linear model with normal, independent errors.
BLUE and MVUE
Understand Best Linear Unbiased Estimation (BLUE) and Minimum Variance Unbiased Estimation (MVUE) in linear models.
Know how to estimate in both the unconstrained and constrained model.
Know how to implement hypothesis tests in the normal linear model.
Cell Means Model
Be able to implement the cell means model in one-way and multiway fixed designs.
Know how to test in a multiple comparison setting.
Lack of Fit
Be able to derive and use measures of lack of fit and importance.
Sums of Squares
Understand the difference and compute Type I and Type III sums of squares.
Understand and be able to compute tests and estimates when a design has data missing in some cells.