**This is a master’s / advanced undergraduate level course in
linear regression methods.**

**Text:**

Required:

__Applied Linear Regression Models__

4th Ed., by Kutner, Nachtsheim, and Neter. McGraw-Hill, 2004.

Strongly recommended:

__Pattern Recognition and Machine Learning__

Christopher M. Bishop. Springer, 2006.

**Prerequisite:**
Calculus; probability and statistics at the level
of W4150, or W4105 and W4107 taken concurrently.

**Corequisite:**
Linear algebra.

**Grading:**
Grades will be assigned on a curve, using the
following percentages: 25% Homework, 25% Midterm, 25% Final, 25% Quizzes.

Homework will include a mix of paper and computer problems. Homework will be assigned in class as we go along; the assignments and due dates will be posted on this webpage. Quizzes will generally be announced in class the class before they happen.

**Midterm:**
The midterm will be during class hours.

**Final:**

**Computing:**
You will be required to use Matlab to complete your homework assignments.

See the ACIS page for
information on software and computing labs.

**Scope:**
This class is about the theory and practice of
regression analysis. The theory will be approached from both the frequentist and Bayesian perspectives and use of the
computer to analyze data will be emphasized. The first part of the
course will focus on the basic techniques with one-dimensional data,
and will assume familiarity with the following topics from
statistics (see appendix A in the book for a quick review, or e.g. Rice
or a similar textbook for more details):

- Gaussian distributions
- Joint, conditional distributions
- Law of large numbers, central limit theorem
- Estimation
- Bias, variance, covariance
- Maximum likelihood
- Hypothesis testing
- Confidence intervals

The second part of the course will look specifically at the challenges posed by multivariate data. We will do a very brief linear algebra review, but it will be essential to be familiar with the following topics from linear algebra:

- Vectors, matrices
- Linear transformations, bases
- Matrix inverse
- Eigenvalues, eigenvectors
- Quadratic forms
- Determinants