Module Details

 The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
 Title Linear Statistical Models Code MATH363 Coordinator Dr K Zychaluk Mathematical Sciences Kamila.Zychaluk@liverpool.ac.uk Year CATS Level Semester CATS Value Session 2021-22 Level 6 FHEQ First Semester 15

Aims

- To understand how regression methods for continuous data extend to include multiple continuous and categorical predictors, and categorical response variables.

- To provide an understanding of how this class of models forms the basis for the analysis of experimental and also observational studies.

- To understand generalized linear models.

- To develop skills in using an appropriate statistical software package.

Learning Outcomes

(LO1) Be able to understand the rationale and assumptions of linear regression and analysis of variance.

(LO2) Be able to understand the rationale and assumptions of generalized linear models.

(LO3) Be able to recognise the correct analysis for a given experiment.

(LO4) Be able to carry out and interpret linear regressions and analyses of variance, and derive appropriate theoretical results.

(LO5) Be able to carry out and interpret analyses involving generalised linear models and derive appropriate theoretical results.

(LO6) Be able to perform linear regression, analysis of variance and generalised linear model analysis using an appropriate statistical software package.

Syllabus

- General Linear Models.

- Simple linear regression; one-way analysis of variance; estimation and inference; two and three-way analysis of variance; more complex designs.

- Generalized Linear Models: Foundations; exponential family of distributions; estimation and inference; binary response variables; normal response variables; contingency tables and log-linear models; other applications.

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

MATH101 CALCULUS I; MATH102 CALCULUS II; MATH162 INTRODUCTION TO STATISTICS; MATH263 STATISTICAL THEORY AND METHODS I; MATH103 INTRODUCTION TO LINEAR ALGEBRA

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Final assessment on campus  60 minutes    40
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
class test 1  around 60-90 minutes    30
class test 2      30