Linear Models

MATH (STAT) 222 Spring 2017 Syllabus

Revised March 27, 2017

Basic Course Information

Professor of Record: Ole J. Forsberg, Ph.D.

Classroom: SMC D-205

Class Hours: Period 2, MWRF

Office: SMC E-212

Office Hours: 2:40–3:50, MWRF

E-Mail Address: ojforsberg@knox.edu

Course Catalog Description

This course develops further the ideas and techniques that were introduced in STAT 200 relative

to regression modeling and experimental design, understood as instances of a matrix linear

model. In addition, the student becomes familiar with at least one leading statistical package for

performing the intensive calculations necessary to analyze data. Topics include linear, non-linear,

and multiple regression, model-building with both quantitative and qualitative variables, model-

checking, logistic regression, experimental design principles, ANOVA for one-, two-, and multiple

factor experiments, and multiple comparisons.

Course Overview

The major purpose of this course is to explore linear models and problems that can be solved

using them. In short, we will return repeatedly to the matrix equation Y = XB + E to gain new

insights into linear modeling.

Course Objectives

By the end of this course, you should be able to

know the logic behind linear models;

use different definitions of “best” to model relationships;

model dependent variables that are numeric, discrete, dichotomous, and categorical;

understand the nomenclature of experimental design;

perform and present novel research; and

use the R statistical environment to perform analyses.

Required textbook and materials

Textbook: Julian J. Faraway. Linear Models with R, second edition.

Chapman and Hall/CRC, 2014.

Alternatively, you can access the pre-print version of

this book, titled “Practical Regression and Anova using R,”

at https://cran.r-project.org/doc/contrib/Faraway-PRA.pdf

Software: As this is an applied statistics course, you will also need

to use a computer to perform some statistical calculations.

Because of its utility and ubiquity in applied statistics, we

will use the R statistical environment.

Computer: As there is a required statistics program, expect to bring

your laptop to class to do statistics.

Behavioral expectations

If your question is “What will it take to succeed in this course?” then the answer is “Simply being

a good student.” In my experience, your ability as a student is the greatest predictor of success

in courses such as this. Being a good student means that you

• read and outline the readings before class;

• ask questions about the readings during class;

• are an active participant during class;

• begin homework as soon as it is assigned;

• are aware of course deadlines;

• spend enough time on the material to learn it;

• are observant;

• use learning techniques you developed in previous courses; and

• recognize your limitations and work to strengthen them.

You are responsible for all material covered during the class period and all material in the readings

and activities. Feel pressured to ask questions during the class regarding the textbook material,

since the material covered during the classes may or may not cover everything that is in the text.

As with most courses at this college, you should be prepared to spend approximately 15

hours per week on the coursework for this class. That includes time inside class and time outside

class. Since you spend 4 hours and 40 minutes in class, you should be willing to spend 10 hours

and 20 minutes outside class in preparing for class, working on homework, reviewing notes, and

anything else associated with the course.

Grading Information

Your grade for this course depends on how well you meet the requirements set forth in the

syllabus. The following section provides information about the various grade inputs. All times are

Galesburg, IL, time (CT).

Homework Assignments

The usual homework assignments are designed to test a small sliver of what we covered in the

course. These assignments may be from the book or from my own mind. Expect homework each

week. You may not get it each week, but feel free to expect it each week.

Term Project

There will be a single project in this course worth 100 points. It will cover a much larger swath of

the material than any single homework assignment. More information on the term project will be

released later.

Examinations

There are three examinations: two intra-term examinations (100 points each) and one final

examination (100 points). No make-ups or postponements are given.

Extra Credit

I do not offer extra credit in this course.

Late Assignments

I do not accept late assignments.

Overall course grade

I calculate your percent in the course by adding all of the points you earned during the semester

and dividing by the total number of points that you could have earned. This percentage is then

used to determine your final letter grade for the course:

A- 90 – 93% A 93 – 97% A+ 97% and above

B- 80 – 83% B 83 – 87% B+ 87 – 90%

C- 70 – 73% C 73 – 77% C+ 77 – 80%

D- 60 – 63% D 63 – 67% D+ 67 – 70%

F Below 60%

I do not round.

Academic Integrity

In this class, assignments should represent your individual effort, unless explicitly stated in the

assignment (e.g., group projects). You may talk with other students and tutors about assignments,

but you should work through the computations and enter the values yourself.

Knox College is committed to the maintenance of the highest standards of integrity and

ethical conduct of its members. This level of ethical behavior and integrity will be maintained in

this course. Should I discover that you participated in a behavior that violates academic integrity

(e.g., unauthorized collaboration, plagiarism, cheating on examinations, fabricating information,

helping another person cheat, unauthorized advance access to examinations, altering or

destroying the work of others, and fraudulently altering academic records), I will prosecute

(sanction) you according to the College rules.

Please read through the Knox College Honor System to familiarize yourself with what

constitutes a violation:

http://www.knox.edu/offices/academic-affairs/honor-code-and-procedures/

Office of Disability Services

Knox College abides by Section 504 of the Rehabilitation Act of 1973 which stipulates that no

student shall be denied the benefits of an education “solely by reason of a handicap.” Disabilities

covered by law include, but are not limited to, learning disabilities, psychological disabilities,

health impairments, hearing, and sight or mobility impairments. If you have a disability that may

have some impact on your work in class and for which you may require accommodations, please

see the Office of Disability Services (located in SMC E-115) so that such accommodations may

be arranged.

Brief Schedule of Topics

The following is a brief schedule for the course. I reserve the right to change this as I see fit. Who

knows, we may find a topic that we want to explore more deeply. If so, we will spend more time

with it. I may also decide to skip a topic, especially if I am bored of it.

Unit I: Ordinary Least Squares

Topics: The linear model, matrix representation, Gauss-Markov theorem, confidence intervals,

inference, confidence bands, regression diagnostics

Unit II: Extending OLS

Topics: Transformation, weighted least squares, generalized linear models, maximum likelihood

estimation, logistic regression

Unit III: Experimental Design

Topics: one-way anova, multiple comparisons, two-way anova, blocking designs, fixed and

random effects, factorial experiments