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HASC413 Biostatistics

2021 information for papers will be published in early September. 

Essential analytical methods necessary for health-related research: data description, basic probability, confidence intervals, hypothesis testing, sample size calculation, epidemiological measures of association, and simple linear regression. A knowledge of basic algebra is recommended.

This paper provides an introduction to the use of statistical methods to answer specific research questions in health-related areas.

Paper title Biostatistics
Paper code HASC413
Subject Health Sciences
EFTS 0.1250
Points 15 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $1,241.25
International Tuition Fees (NZD) $5,181.50

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Limited to
MHealSc, MPharm, MSc, PGDipHealSc, PGDipMLSc, PGDipSci, PGDipPharm
Suitable for students from any disciplines who are interested in learning introductory level biostatistics. A basic knowledge of algebra is assumed.
Teaching staff
Convenor and Lecturer: Jiaxu Zeng
Paper Structure
This paper covers the following main topics:
  1. Introduction to epidemiological studies
  2. Descriptive statistics and graphical summaries
  3. Introduction to probability
  4. Probability distributions
  5. Sampling distributions
  6. Confidence intervals
  7. Hypothesis testing
  8. Power and sample size calculation
  9. Analyses for epidemiological studies
  10. Simple linear regression
Teaching Arrangements

Lectures twice a week, one 1.5 hours, one 50 minutes, plus computer laboratory work, 2 hours

Kirkwood, BR & Sterne, AC (2003) Essential Medical Statistics
Graduate Attributes Emphasised
Communication, Critical thinking, Research.
View more information about Otago's graduate attributes.
Learning Outcomes
  1. Perform basic statistical analyses to address simple research questions using a statistical software package (Stata)
  2. Understand the principles behind basic statistical analyses
  3. Understand how to interpret and present basic statistical analyses

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First Semester

Teaching method
This paper is taught On Campus
Learning management system

Computer Lab

Stream Days Times Weeks
A1 Friday 10:00-11:50 9-12, 18-22


Stream Days Times Weeks
A1 Monday 14:00-15:50 9-13, 19-22
Tuesday 15:00-15:50 9-12, 18-22