Linear Models

Linear Models
Theory of estimation and testing in linear models. Analysis of full-rank model, over-parameterized model, cell-means model, unequal subclass frequencies, and missing and fused cells. Estimability issues, diagnostics.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesDepartmental consent.
Course Outcomes

Course Outcomes

Upon successful completion of this course, the student will be able to:

Understand Derivation

Understand derivation and distribution of linear and quadratic forms.

Understand Definitions

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.


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.

Hypothesis Tests

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.

Multiple Comparison

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.

Missing Cells

Understand and be able to compute tests and estimates when a design has data missing in some cells.