Linear Models

Victoria University of Wellington

Course Description

  • Course Name

    Linear Models

  • Host University

    Victoria University of Wellington

  • Location

    Wellington, New Zealand

  • Area of Study

    Mathematics, Statistics

  • Language Level

    Taught In English

  • Prerequisites

    (MATH 243, MATH 277/STAT 233) or (STAT 293, 391)

  • Course Level Recommendations

    Upper

    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • Credit Points

    15
  • Recommended U.S. Semester Credits
    3 - 4
  • Recommended U.S. Quarter Units
    4 - 6
  • Overview

    This course will cover general linear models: theory and applications, including maximum likelihood estimation, model selection, AIC, tests of hypotheses, confidence intervals, and residual diagnostics. It will also describe the theory of generalised linear models and give examples for binary and count data. The statistical software R will be used.

    Course content
    This course covers general linear models: theory and applications, including maximum likelihood estimation, model selection, AIC, tests of hypotheses, confidence intervals, and residual diagnostics. It also describes the theory of generalised linear models and will give examples for binary and count data. The statistical software package R will be used.


    Course learning objectives
    Students who pass this course will be able to:

    • Describe the basic theory of linear models, including the construction of design matrices;
    • Estimate model parameters by maximum likelihood;
    • Select models using F tests and AIC;
    • Derive statistics for tests of linear hypotheses, and carry out such tests;
    • Construct confidence intervals for model parameters;
    • Compute and interpret residual diagnostics;
    • Describe and apply the basic theory of generalised linear models for binary and count data;
    • Describe and apply the basic theory of longitudinal analysis for continuous responses using fixed or random effects models.

Course Disclaimer

Courses and course hours of instruction are subject to change.

Eligibility for courses may be subject to a placement exam and/or pre-requisites.

Some courses may require additional fees.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

Please reference fall and spring course lists as not all courses are taught during both semesters.

Availability of courses is based on enrollment numbers. All students should seek pre-approval for alternate courses in the event of last minute class cancellations

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.