625.661.81 - Statistical Models and Regression

Applied and Computational Mathematics
Spring 2024


Introduction to regression and linear models including least squares estimation, maximum likelihood estimation, the Gauss-Markov Theorem, and the Fundamental Theorem of Least Squares. Topics include estimation, hypothesis testing, simultaneous inference, model diagnostics, transformations, multicollinearity, influence, model building, and variable selection. Advanced topics include nonlinear regression, robust regression, and generalized linear models including logistic and Poisson regression.


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H.M. James Hung


Course Structure

The course materials are divided into modules which can be accessed by clicking Modules. A module will have several sections including the overview, content, readings, discussions, or assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for due dates and Exams.

Course Topics

Introduction to regression modeling; simple linear regression model; multiple linear regression model; polynomial regression; indicator variables; model adequacy checking; transformation and weighting; model diagnostics; model building; model validation; generalized linear model; nonlinear regression; nonparametric regression.

Course Goals

To introduce statistical methods for regression analysis and model building for studying relationship between variables and develop tools for model diagnostics and validation and then apply these methods and tools to real case studies.

Course Learning Outcomes (CLOs)


Montgomery, D.C., Peck, E. A., Vining, G. G. (2021). Introduction to linear regression analysis (6th ed.). Hoboken, NJ: John Wiley & Sons, Inc.

ISBN 13: 978-1-119-57872-7    

ISBN 10: 1-119-57872-8

Textbook information for this course is available online through the appropriate bookstore website: For online courses, search the MBS website at http://ep.jhu.edu/bookstore.

Required Software

There is no required software to purchase. You are free to use any mathematical or statistical software, such as MATLAB, R, SAS, MINITAB, web-based statistical software, to help with computations.

Student Coursework Requirements

It is expected that each module will take approximately 7-11 hours per week to complete. Here is an approximate breakdown: listening to the audio annotated slide presentations (approximately 1-2 hours for each module), reading the assigned sections of the texts (approximately 2-3 hours for each module), discussion forums (1 hour). If the module includes problem set assignment, it will take approximately 2-5 hours. Exam #1 will be given in the week between Module 4 and Module 5, and Exam #2 in the week between Module 10 and Module 11. Each exam will take approximately 7-11 hours.

This course will consist of the following basic student requirements:

Discussion Forum (20% of Final Grade Calculation)

You are responsible for carefully reading all assigned material and being prepared for discussion. The majority of readings are from the course text. Additional reading may be assigned to supplement text readings.

Post your initial response to the discussion questions by the evening of Day 3 for that module week; for example, if Day 1 of the module is Wednesday, Day 3 is Friday of that module week. Posting a response to the discussion question is part one for module discussions (i.e., Timeliness).

Part two for module discussion is your interaction (i.e., responding to classmate postings with thoughtful responses) with at least two classmates (i.e., Critical Thinking). Just posting your response to a discussion question is not sufficient; I want you to interact with your classmates. Be detailed in your postings and in your responses to your classmates' postings. Feel free to agree or disagree with your classmates. Please ensure that your postings are civil and constructive.

Posting response for your interaction in Part two is normally due Day 5 for that module week.
Evaluation of preparation and participation is based on contribution to discussions.

Discussion forum is evaluated by the following grading elements:

  1. Timeliness (45%)
  2. Critical Thinking (40%)
  3. Approximate Correctness in your initial response (15%) 

Assignments (30% of Final Grade Calculation)

Assignments will include quantitative problem sets for analytical derivation or statistical analyses. Include a cover sheet with your name and assignment identifier. Also include your name and a page number indicator (i.e., page x of y) on each page of your submissions. Each problem should have the problem statement, assumptions, computations if applicable, and conclusions/discussion delineated. All Figures and Tables should be captioned and labeled appropriately.

You are expected to work on all assignments independently.

All assignments are due according to the dates in the Calendar.

Late submissions will be reduced by one letter grade for each week late (no exceptions without prior coordination with the instructor).

Quantitative assignments are evaluated by the following grading elements:

  1. Each part of problem is answered.
  2. Assumptions are clearly stated.
  3. Intermediate derivations and calculations are provided in detail.
  4. Answer is technically correct and is clearly indicated.

Exams (30% of Final Grade Calculation)

Exam #1 will be given in the week between Module 4 and Module 5, and Exam#2 in the week between Module 10 and Module 11. You will have seven days to complete each exam and it will be due by the time specified on the Calendar. You may use the course text to complete the exams. You are expected to work on all the exams independently.

The exams are evaluated by the following grading elements:

  1. Each part of problem is answered.
  2. Assumptions are clearly stated.
  3. Intermediate derivations and calculations are provided.
  4. Answer is technically correct and is clearly indicated.

Course Project (20% of Final Grade Calculation)

A course project will be assigned in Module 7 and will be due according to the date specified in the Course Calendar.  You are expected to work on the course project independently.

The course project is evaluated by the following grading elements:

  1. Student preparation and participation (as described in Course Project Description).
  2. Student technical understanding of the course project topic.
  3. Student building regression models with rationales and assessing the predictivity of the models.
  4. Student making statistical inferences for the study objectives and interpreting analysis results.
  5. Student writing a report of the course project.

Grading Policy

Assignments, exams, and course project are due according to the dates posted in your course site. You may check these due dates in the Course Calendar.

The final grade may be determined by the final score distribution (e.g., curve) of the class(s).

Final grades will be determined by the following weighting:


% of Grade

Discussion Forums






Course Project


A grade of A indicates achievement of consistent excellence and distinction throughout the course - that is, conspicuous excellence in all aspects of assignments and discussion in every week, exams and the course project.

A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.

Score Range

 Letter Grade


= A+


= A


= A-


= B+


= B


= B-


= C+


= C


= C-


= D+


= D

< 63

= F



Course Policies

Students are required to work independently on the assignments, exams, and course project. Students are encouraged to work collaboratively with classmates on discussion forums.

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.