Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: DTS206TC
Module Title: Applied Linear Statistical Models
Module Level: Level 2
Module Credits: 5.00
Academic Year: 2021/22
Semester: SEM2
Originating Department: Shool of AI and Advanced Computing
Pre-requisites: N/A
   
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 form the basis for the analysis of experimental and also observational studies to understand generalized linear models.


To understand how the Bayesian framework is applied to linear regression model.

Learning outcomes 
A. Demonstrate understanding of the concepts of: a random variable, a distribution, a statistical model, a parameter, a fitted value and a residual

B. Demonstrate understanding of the significance of linear regression models and ANOVA tables

C. Understand the rationale and assumptions of linear regression models

D. Understand the rationale and assumptions of generalized linear models.

E. Carry out and interpret linear regressions and analyses of variance, and derive basic theoretical results.

F. Carry out and interpret analyses involving generalized linear models and derive basic theoretical results.

G. Understand how the Bayesian framework is applied to linear regression models.
Method of teaching and learning 
This module will be delivered by a combination of formal lectures and tutorials.
Syllabus 
- Introduction: Concepts of a random variable, a distribution, a statistical model, a parameter, a fitted value and a residual (2 lectures)

- normal (univariate and multivariate) distribution, t-distribution, Chi-squared distribution, F-distribution and their relationships (3 lectues)

- Estimation: Confidence Interval, Hypothesis Testing, Maximum Likelihood Estimation (4 lectures)


- estimation and inference for the Simple linear regression; estimation and inference for the general linear model. Diagnostic tests using residuals (8 lectures)

- Analysis of Variance and Experimental Designs: Randomization; one-way analysis of variance; two and three-way analysis of variance; factorial designs Applications of Experimental Designs (12 lectures)

- Generalized Linear Models: Foundations; estimation and inference for the generalized linear model; Diagnostic tests using residuals (8 lectures)


- Bayesian Linear Regression (2 lectures)
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 39    13      98  150 

Assessment

Sequence Method % of Final Mark
1 Formal Examination 85.00
2 Assignement 15.00

Module Catalogue generated from SITS CUT-OFF: 6/5/2020 5:43:18 PM