STAT 331: Applied Linear Models

应用线性模型代写 Regression modeling is widely used in applied disciplines, to infer the relationship between a variable of interest (the response…

Course Description:

Regression modeling is widely used in applied disciplines, to infer the relationship between a variable of interest (the response variable) and one or more other variables that are potentially associated with the response variable (explanatory variables).

This course focuses primarily on linear regression models, and we plan to cover the following topics:

  • simple and multiple linear regression
  • least squares estimation for parameters
  • hypothesis testing
  • prediction
  • model diagnostics
  • variable selection
  • using R to perform statistical analyses
  • nonlinear regression and other extensions (if time permits)

Course Objectives: 应用线性模型代写

By the end of the course, students should be able to:

  • Discuss and apply regression models to a variety of real-world problems.
  • Formulate appropriate statistical techniques for solving such problems using real datasets; understand the mathematical foundations for those techniques.
  • Carry out computational analyses on real data; interpret, visualize, and communicate results.



  • There is no required textbook. All necessary course materials will be made available on Learn.
  • Optional reference book: Introduction to Regression Modeling by Bovas Abraham and Johannes Ledolter, 2006. The fifirst four chapters of the book will be made available on Learn for your reference.

Evaluation: 应用线性模型代写

Assessment will be based on three assignments (10% each), four tests (10% each), and a fifinal exam (30%), as detailed below. All dates and deadlines are Eastern Time at Waterloo.


There will be three assignments given during the course, each worth 10% of your fifinal grade. They will be a combination of written solutions and computation, with a focus on data analysis. The due dates of the assignments tentatively are as follows:

  • Assignment 1: Due 11:59pm Friday, October 8
  • Assignment 2: Due 11:59pm Friday, October 29
  • Assignment 3: Due 11:59pm Friday, November 12

Assignments will be released two weeks before the due date and must be submitted via Crowdmark. You may discuss assignments with other students but you must submit your own unique solution written in your own words. Assignments up to 24 hours late will receive a penalty of 50%. Assignments more than 24 hours late will not be graded, and you will receive a zero.

If you miss an assignment due to illness/extenuating circumstances with proper documentation, then the weight for that assignment will be shifted to the other course components.

Tests: 应用线性模型代写

Four 1-hour tests will be given during the courses, consisting of written questions to assess your understanding of the course material. You will have a 24-hour window in which to complete each test.

To account for any technical issues and time needed to upload your work, you will be allowed a total of 1.5 hours to submit your solutions after opening the test. The tests will be given on Crowdmark and are open book. Collaboration is not permitted  on the tests, and no late submissions will be accepted beyond the 1.5 hours given. Do not wait until the last minute to upload!

The tentative test dates are as follows:

  • Test 1: Friday, October 1
  • Test 2: Friday, October 22
  • Test 3: Friday, November 5
  • Test 4: Friday, November 19

If you miss a test due to illness/extenuating circumstances with proper documentation, then the weight for that test will be shifted to the other course components.

Remarking of Assignments/Tests: 应用线性模型代写

If you have a dispute with your grade on an assignment or test, it may be submitted to be remarked within 7 days of the assignment/test being returned to you. Send a formal email to your instructor that contains your name and student ID, clearly stating you request a remark. The entire assignment/test is then subject to be remarked, and it is possible you may gain or lose marks on appeals.

Final exam:

A cumulative fifinal exam will be held online during the fifinal exam period, to be scheduled by the Registrar’s Offiffiffice. It will be 2.5 hours long, and you will have 24-hour time window in which to write the exam.



A great place to get help with course material that you do not understand is the discussion forum for this course on Piazza. It enables you to learn from the questions of others, and to avoid asking questions that have already been asked and answered.

The Piazza forum for this course is located at

Questions about course material should be posted to Piazza rather than  emailing the instructor or the TAs. Do not expect the instructor or TAs to answer questions about course material via email. If you have a question or concern of a personal nature, do contact the instructor directly.

Health Issues: 应用线性模型代写

If you cannot submit your assignment or test solution in time due to a severe illness, please contact the instructor within two days. If you reside in Waterloo, a completed University of Waterloo Verifification of Illness form must be submitted to the instructor within two days. If you reside outside Waterloo, please fifind a local health provider for a verifification form.

UW Academic Policies:

The following are the University of Waterloo’s Academic Policies that all students and professors must comply with.

Academic Integrity: In order to maintain a culture of academic integrity, member of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility.

See: for more information.

Discipline: A student is expected to know what constitutes academic integrity to avoid committing an academic offffence, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offffence, or who needs help in learning how to avoid offffences

(e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate Associate Dean. For information on categories of offffences and types of penalties, students should refer to Policy 71, Student Disci pline, See: guidelines/penaltyguidelines.htm for guidelines for the assessment of penalties. 应用线性模型代写

Avoiding Academic Offffenses: For more information on commonly misunderstood academic offenses and how to avoid them, students should refer to the Faculty of Mathematics Cheating and Student Academic Discipline Policy. See: regulations-and-procedures/cheating-and-student-academic-discipline-guidelines.

Grievance: A student who believes that a decision affffecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. See Policy 70, Student Petitions and Grievances, Section 4: policies-procedures-guidelines/policy-70. When in doubt, please contact the department’s administrative assistant who will provide further assistance.

Appeals: A decision made or penalty imposed under Policy 70 (Student Petitions and Grievances)

(other than a petition) or Policy 71 (Student Discipline) may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72 (Student Appeals).See:

Mathematics Faculty INC Grade Policy: A grade of INC is awarded to a student who has completed course work during the term well enough that they could reasonably be expected to earn a passing mark in the course, but who was unable to complete end-of-term course requirements (usually the fifinal exam) for reasons beyond his or her control. See: current-undergraduates/regulations-and-procedures/incomplete-procedure.

AccessAbility Services: AccessAbility Services, located in Needles Hall, Room 1132, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the AccessAbility Services at the beginning of each academic term.