64635 STATISTICAL MODELS

Year	No.	Offer	Mode	Description			Cred. Pts
97	64635 	S2  	D 	STATISTICAL MODELS        	1.00

Contents

• Staffing
• Pre-requisite(s)
• Co-requisite(s)
• Rationale
• Synopsis
• Objectives
• Topics
• Texts
• Recommended References
• Assessment Details
• Other Requirements

STAFFING:

PRE-REQUISITE(S)

75612/64612 + 75626/64626

CO-REQUISITE(S)

75631/64631

RATIONALE:

Linear Models are very widely used statistical tools.  This unit gives the
student an introduction to the theory of linear models and their applications.
An appropriate computer package is used to give students practice at handling
many data sets.  There are many situations where the usual model assumptions
are not satisfied and special techniques are required and this unit also
introduces some of these techniques.

SYNOPSIS:

This unit introduces the student to statistical linear modelling utilizing
an appropriate computer package.  The topics include linear statistical
models, fitting the full rank model, inferential procedures, residuals and
outliers, multicollinearity, autocorrelations, generalized linear models,
and analysis of categorical data.

OBJECTIVES:

On completion of this unit, students will be able to:

1. construct an appropriate linear model for a data set;
   
2. use an appropriate computer package to fit the model;
   
3. test the constructed model for goodness of fit and
   significance of parameters;
   
4. examine the residuals from the model and interpret residual
   plots;
   
5. recognise the existence of possible outliers and how to deal
   with them detect and deal with outliers;
   
6. understand the concepts and procedure related to the selection
   of the model;
   
7. handle the problem of autocorrelation and multicollinearity.
   
8. analyse and interpret categorical data using a log-linear
   model.
   

TOPICS:

 Description                                                    Weighting(%)

Multiple Regression: model specification, least                       35.00
squares estimators, maximum likelihood
estimators, internal estimation, prediction,
analysis of variance, coefficient of
determination, multiple and partial
correlation, tests of hypottuses,test of
goodness of fit.

Model selection and checking: selection of                            20.00
best model, residual analysis,
transformations for variance stabilisation,
diagnostic checking.

Multicollinearity:sources and consequences,                           10.00
identification and solution of multicollinear
problem, ridge regression.

Autocorrelation:sources and consequences,                             10.00
model for first order autocorrelation, tests
for the presence of autocorrelation,
estimated generalised least squares estimator,
prediction

Generalised Linear Model: systematic and random                       15.00
component, likelihood functions, link
function, deviance, applications, generalised
additive model

Analysis of Categorical Data: log-linear model                        10.00
for contingency model

TEXT and MATERIALS to be PURCHASED:

RECOMMENDED REFERENCE MATERIALS:

Barker, R.J. & Nelder, J.A. 1983, The GLIM System Release 4, NAG,
Oxford.

Cox, D.R. 1977, Analysis of Binary Data, Chapman & Hall, London.

Dobson, A.J. 1990, An Introduction to Generalized Linear Models,
Chapman and Hall, London.

Draper, N. & Smith, H. 1981, Applied Regression Analysis, 2nd edn,
Wiley, New York.

Everitt, B.S. 1977, The Analysis of Contingency Tables, Chapman
Hall, London.

Graybill, F.A. 1961, An Introduction to Linear Statistical Models,
McGraw-Hill, New York.

Montgomery, D.C. & Peck, E.A. 1992, Introduction to Linear Regression
Analysis, 2nd edn, Wiley, New York.

Searle, S.R. 1971, Linear Models, Wiley, New York.

ASSESSMENT DETAILS:

No  *F/S Marks     Due        Description                              Wtg(%)    LBL
1   S              21/08/97  ASSIGNMENT 1                              10.00     N
2   S              19/09/97  ASSIGNMENT 2                              10.00     N
3   S              17/10/97  ASSIGNMENT 3                              10.00     N
4   S              END S2    3 HR CLSD BK THEORY EXAMINATION           70.00     N

OTHER REQUIREMENTS:

1    To   obtain   a   pass  in  the  unit,  students   must   perform
     satisfactorily in all aspects of assessment.
2    The  due date for assessments is the date by which a student must
     despatch an assignment to the USQ. The onus is on the student  to
     provide proof of the despatch date, if requested by the Examiner.
3    Students  MUST  retain a copy of all assignments  which  must  be
     produced if and when required by the Examiner.
4    Extensions   for  assignment  submission  may   be   granted   in
     extenuating  circumstances. The decision to grant  or  refuse  an
     extension is made by the Examiner. Students should be aware  that
     an  application  for  an  extension does not  guarantee  that  an
     extension will be granted.
5    Students  apply for extension by either applying at the  time  of
     submitting  an  assignment  or  applying  in  writing  prior   to
     submitting  an  assignment.  All  relevant  documentation  should
     accompany the application.
6    If  assignments are submitted after the due date and no extension
     is  granted,  then  a  penalty up to a  maximum  of  20%  of  the
     assignment mark for each working day late may apply.
7    No  further assignments will be accepted for assessment  purposes
     after  assignments or model solutions have been released,  except
     in extenuating circumstances.
8    The  decision of the Dean shall be final on any dispute that  may
     arise in the implementation of these guidelines.