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STAT210 Statistical Methods 2

Linear regression, factorial analysis of variance, modelling binomial and count data; cluster analysis, principal component analysis, replication and pseudo-replication, randomisation and blocking, stratification and clustering.

A service paper in statistics that builds on the introduction that many students receive in either STAT 110 Statistical Methods or STAT 115 Introduction to Biostatistics.

Paper title Statistical Methods 2
Paper code STAT210
Subject Statistics
EFTS 0.1500
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees (NZD) $851.85
International Tuition Fees (NZD) $3,585.00

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Prerequisite
STAT 110 or STAT 115 or (BSNS 102 or BSNS 112)
Schedule C
Arts and Music, Science
Notes
STAT 210 cannot be credited to a major or minor in Statistics if ECON 210, FINC 203, FINC 308, STAT 241, STAT 242, STAT 251, or STAT 342 has been passed previously or is being taken concurrently.
Eligibility
Intended for students not majoring in Statistics who have exposure to introductory statistical methods.
Contact
twang@maths.otago.ac.nz
Teaching staff
Dr Matthew Schofield
Paper Structure
The paper covers three key themes:
  • Regression modelling
  • Multivariate analysis
  • The design of research studies
Teaching Arrangements
One 2-hour lecture and one 2-hour streamed tutorial per week
Textbooks
Textbooks are not required for this paper.
Graduate Attributes Emphasised
Communication, Critical thinking, Information literacy, Research.
View more information about Otago's graduate attributes.
Learning Outcomes
Students who successfully complete the paper will
  • Apply a range of important statistical methods to real data
  • Understand the assumptions underlying use of these methods
  • Assess what types of statistical methods are valid for different kinds of data
  • Be aware of the issues involved in designing a research study
  • Critically appraise research literature in terms of statistical methods used
  • Use a standard statistical programming language (R) to analyse data

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Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
L1 Wednesday 11:00-12:50 28-34, 36-41

Tutorial

Stream Days Times Weeks
Attend one stream from
T1 Monday 11:00-12:50 29-34, 36-41
T2 Tuesday 15:00-16:50 29-34, 36-41
T3 Thursday 15:00-16:50 29-34, 36-41
T4 Friday 11:00-12:50 29-34, 36-41
T5 Friday 14:00-15:50 29-34, 36-41

Linear regression, factorial analysis of variance, modelling binomial and count data; cluster analysis, principal component analysis, replication and pseudo-replication, randomisation and blocking, stratification and clustering.

A service paper in statistics that builds on the introduction that many students receive in either STAT 110 Statistical Methods or STAT 115 Introduction to Biostatistics.

Paper title Statistical Methods 2
Paper code STAT210
Subject Statistics
EFTS 0.1500
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees Tuition Fees for 2018 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Prerequisite
STAT 110 or STAT 115 or (BSNS 102 or BSNS 112)
Schedule C
Arts and Music, Science
Notes
STAT 210 cannot be credited to a major or minor in Statistics if ECON 210, FINC 203, FINC 308, STAT 241, STAT 242, STAT 251, or STAT 342 has been passed previously or is being taken concurrently.
Eligibility
Intended for students not majoring in Statistics who have exposure to introductory statistical methods.
Contact
twang@maths.otago.ac.nz
Teaching staff
Dr Matthew Schofield
Paper Structure
The paper covers three key themes:
  • Regression modelling
  • Multivariate analysis
  • The design of research studies
Teaching Arrangements
One 2-hour lecture and one 2-hour streamed tutorial per week.
Textbooks
Textbooks are not required for this paper.
Graduate Attributes Emphasised
Communication, Critical thinking, Information literacy, Research.
View more information about Otago's graduate attributes.
Learning Outcomes
Students who successfully complete the paper will
  • Apply a range of important statistical methods to real data
  • Understand the assumptions underlying use of these methods
  • Assess what types of statistical methods are valid for different kinds of data
  • Be aware of the issues involved in designing a research study
  • Critically appraise research literature in terms of statistical methods used
  • Use a standard statistical programming language (R) to analyse data

^ Top of page

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
L1 Wednesday 11:00-12:50 28-34, 36-41

Tutorial

Stream Days Times Weeks
Attend one stream from
T1 Monday 11:00-12:50 29-34, 36-41
T2 Tuesday 15:00-16:50 29-34, 36-41
T3 Thursday 15:00-16:50 29-34, 36-41
T4 Friday 11:00-12:50 29-34, 36-41
T5 Friday 14:00-15:50 29-34, 36-41