Fall 2022

This page was updated on August 21, 2022.


Richard L. Smith
Hanes 303
Email: rls "at" email "dot" unc "dot" edu
Home Web Page

Class Time and Place

Tuesdays and Thursdays, 11:00 am - 12:15 pm.

Location: Hanes 125

The first class is on Tuesday, August 16, and the last class is on Tuesday, November 29. There will be no class on Tuesday September 6 (Well Being Day) or Thursday October 20 (Fall Break) or Thursday November 24 (Thanksgiving). If you need accommodation on other holidays that I am not yet aware of, please discuss it with me. Any changes to these arrangements will be discussed in advance with the class.

Students should regularly check the course web and sakai pages, where the new materials will be posted as the course proceeds.

Office Hours

Three office hours per week (shared with STOR 557), Mondays, 1:00 - 3:00 pm; Tuesdays 3:00-4:00 pm; Wednesdays, 12:00-1:30 pm. Students may attend office hours in person or on zoom ( I may from time to time have to cancel or reschedule office hours and will make every attempt to inform the class in advance; in any case, you are always welcome to contact me by email.

Instructional Assistant

Jiaying Li (jyingli "at" email "dot" unc "dot" edu). Office hour Mondays 9:00-11:00 am by zoom:

COVID-19 Policies

There is no requirement that students at UNC must either wear a face mask or be vaccinated. Nevertheless, I urge you to be cautious and considerate of your fellow students (and me) on these matters, especially if you think you might have been exposed to the COVID virus. Please remember that the pandemic is NOT over and people are still regularly testing positive. My recommendation is that you continue to wear a face mask in most indoor settings where you are with people who are not family members or close friends, including this class. As for me, I plan to deliver my classes without a face mask so long as I have no symptoms myself and can maintain a sufficient distance from the students, but expect me to wear a mask when holding meetings in my office or elsewhere.

If you test positive for COVID, or have symptoms, or have reasonable grounds to think you have been exposed, please do not attend class but send me a note about it (there is no need to obtain a formal excuse for occasional absences). I will make whatever accommodations are needed for students who miss class because of illness.

As for vaccinations, I don't think there is anything new to be said. If you have not been vaccinated, I strongly urge you to consider it. Vaccines for students, faculty and staff are offered free of charge at Student Stores Pharmacy on the top floor of UNC Student Stores (bring your insurance card).

Course Texts

The course texts are (a) the draft version of Linear Regression by R.L. Smith and K.D.S. Young (available as a course pack through Student Stores); (b) Linear Models with R (Second Edition)
by Julian Faraway. This will also be available through Student Stores; you are welcome to obtain it from a different supplier but make sure you get the second edition.

For registered participants, the Smith and Young course text is also available for free (pdf file) through the sakai page. Please do not pass this on to others, but feel free to refer any queries to me.

I have created a data page that links to datasets and programs from the Smith and Young text that will be used in the course.

Chapter Headings (Smith and Young)

The syllabus for the course is undergoing revisions following a recent review of the department's Applied Statistics offerings. What follows here is essentially the list of topics as covered by the current version of the Smith and Young text; it is planned to adjust some topics as we go through the material.

Chapter 1: Air pollution and public health: A case study for regression analysis.
This introductory chapter discusses a major public policy issue where the use (or, depending on your point of view, misuse) of regression analysis has featured heavily. It illustrates some of the techniques which we will be discussing in detail later in the course, and also describes some of the pitfalls associated with the use of regression to solve substantive scientific problems.

Chapter 2: Simple linear regression.
For most of you, much of this material will be revision, covering the simple case of one y variable and one x variable. However, we also discuss some more subtle features, such as simultaneous confidence intervals, inverse regression or calibration, and tests for autocorrelation.

Chapter 3: Multiple regression.
Matrix formulation and solutions. Confidence and prediction intervals, and hypothesis tests. Simultaneous estimation. Power of the F test. Examples. The chapter concludes with an outline of the geometric approach to least squares theory, with the aid of which we are able to provide slick proofs of all the major mathematical results.

Chapter 4: Diagnostics for influential observations.
This chapter is concerned with the effect of outliers among either the x or y values. The hat matrix. Diagnostics for influence: DFFITS, DFBETAS, Cook's statistic, COVRATIO. Graphical methods. Examples.

Chapter 5: Diagnostics for model selection.
Multicollinearity. Variable selection. Transformations. Applications. To be added: LASSO regression.

Chapter 6: Two Case Studies.
(a) Air pollution and daly mortality in Birmingham, Alabama. (b) The Bush-Gore Election from 2000.

Chapter 7: Miscellaneous topics in regression.
Weighted and Generalized least squares. Response surface methodology. Introduction to nonlinear regression.

Chapter 8: Analysis of Designed Experiments.
One-way and two-way analysis of variance, interaction, analysis of covariance. To be added: latin squares, factorial designs.


The course includes an extensive practical computing component. Previous versions of the class (including the Smith-Young coursepack) included examples in both R and SAS, but the department has now decided not to try to teach SAS, so the computing elements of the course will be entirely in R. If you do not have it already, you should download it from

If you prefer R-Studio, that is also completely acceptable; most of the examples work exactly the same way in R and R-Studio (though the appearance of the output may differ).

The intent of the course is not to teach R from first principles; I assume most if not all students are already familiar with it, but if not, I recommend using the Faraway text and following up further references there if they are needed.

Assignments and Exams

Homeworks consisting of both theoretical and computational exercises will be set, at approximately two-week intervals. There will be a midterm and a final exam. The midterm I am tentatively scheduling for Thursday, October 6, in class (midpoint of the semester) though I may change this after consultation with the class; please let me know as early as possible if you have any conflict for that date. The final exam is scheduled by the Registrar's office for 8:00-11:00 am on Saturday, December 3, though it is possible I will switch to a takehome format - this will be fully discussed with the class well before the exam itself.

Following the recent review of the curriculum, it is also planned to include an individual student project component of the course. Provisional distribution of marks: 20% for homework assignments, 25% for the midterm, 25% for the project, 30% for the final exam.

Further reading

Other references that may be helpful include the following:

Atkinson, A.C. (1985), Plots, transformations, and regression. Oxford : Oxford University Press. QA278.2 .A85 1985
Cook, R.D. and Weisberg, S. (1982), Residuals and influence in regression. New York : Chapman and Hall. QA278.2 .C665 1982
Cook, R.D. and Weisberg, S. (1999), Applied regression including computing and graphics. New York : Wiley. QA278.2 .C6617 1999
Dean, A. and Voss, D. (1999), Design and analysis of experiments. New York : Springer. QA279 .D43 1999
Draper, N.R. and Smith, H. (1998), Applied Regression Analysis (Third Edition). New York: Wiley. QA278.2 .D7 1998
Faraway, J.J. (2016), Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Second Edition, Chapman and Hall.
McCullagh, P. and Nelder, J.A. (1989), Generalized linear models. London : Chapman and Hall. QA276 .M38 1989
Neter, Kutner, Nachtsheim and Wasserman (1996), Applied Linear Statistical Models. Fourth Edition: Irwin, Chicago. QA278.2 .A66 1996
Rawlings, J.O., Pantula, S. and Dickey, D.A. (1998), Applied regression analysis : a research tool. New York : Springer. QA278.2 .R38 1998
Scheffe, H. (1959), The analysis of variance. New York : Wiley. QA276 .S34
Weisberg, S. (1985), Applied linear regression. New York : Wiley. QA278.2 .W44 1985

Pro Forma Items

Accessibility Resources

The University of North Carolina at Chapel Hill facilitates the implementation of reasonable accommodations, including resources and services, for students with disabilities, chronic medical conditions, a temporary disability or pregnancy complications resulting in barriers to fully accessing University courses, programs and activities.

Accommodations are determined through the Office of Accessibility Resources and Service (ARS) for individuals with documented qualifying disabilities in accordance with applicable state and federal laws. See the ARS Website for contact information: or email (source:

Counseling and Psychological Services (CAPS)

CAPS is strongly committed to addressing the mental health needs of a diverse student body through timely access to consultation and connection to clinically appropriate services, whether for short or long-term needs. Go to their website: or visit their facilities on the third floor of the Campus Health Services building for a walk-in evaluation to learn more. (source: Student Safety and Wellness Proposal for EPC, Sep 2018)

Title IX Resources

Any student who is impacted by discrimination, harassment, interpersonal (relationship) violence, sexual violence, sexual exploitation, or stalking is encouraged to seek resources on campus or in the community. Please contact the Director of Title IX Compliance (Adrienne Allison -, Report and Response Coordinators in the Equal Opportunity and Compliance Office (, Counseling and Psychological Services (confidential), or the Gender Violence Services Coordinators (; confidential) to discuss your specific needs. Additional resources are available at

Honor Code

For the complete honor code, please visit

It shall be the responsibility of every student enrolled at the University of North Carolina to support the principles of academic integrity and to refrain from all forms of academic dishonesty, including but not limited to, the following:

1. Plagiarism in the form of deliberate or reckless representation of another's words, thoughts, or ideas as one's own without attribution in connection with submission of academic work, whether graded or otherwise.
2. Falsification, fabrication, or misrepresentation of data, other information, or citations in connection with an academic assignment, whether graded or otherwise.
3. Unauthorized assistance or unauthorized collaboration in connection with academic work, whether graded or otherwise.
4. Cheating on examinations or other academic assignments, whether graded or otherwise, including but not limited to the following:
(a) Using unauthorized materials and methods (notes, books, electronic information, telephonic or other forms of electronic communication, or other sources or methods);
(b) Violating or subverting requirements governing administration of examinations or other academic assignments;
(c) Compromising the security of examinations or academic assignments;
(d) Representing another's work as one's own; or
(e) Engaging in other actions that compromise the integrity of the grading or evaluation process.
5. Deliberately furnishing false information to members of the University community in connection with their efforts to prevent, investigate, or enforce University requirements regarding academic dishonesty.
6. Forging, falsifying, or misusing University documents, records, identification cards, computers, or other resources so as to violate requirements regarding academic dishonesty.
7. Violating other University policies that are designed to assure that academic work conforms to requirements relating to academic integrity.
8. Assisting or aiding another to engage in acts of academic dishonesty prohibited in the above items.

Administrative details

All questions regarding course registration and waiting list should be directed at Ms. Christine Keat,

The instructor reserves to right to make changes to the syllabus.

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