Introduction to the use of statistical methods in health sciences research. Descriptive and simple inferential statistics for discrete, continuous and right-censored data. Introduction to linear regression.
This distance paper will introduce students to the use of statistical methods in health
sciences research and is highly recommended for all students that want and/or need
to analyse quantitative data. Students will learn the theory needed to perform basic
descriptive analysis as well to correctly understand appropriate statistical methods
to test quantitative questions. The paper has a strong applied component, and students
will learn how to perform analysis with computational software, particularly how to
generate high-quality plots and report results for thesis and scientific journals.
Topics covered include: data management, descriptive statistics, hypothesis testing
and introduction to study design. For this paper, students must have a computer with
an Internet connection and be computer literate.
Students will learn R, an open-source, free statistical software under the terms of the GNU General Public License.
|Paper title||Applied Biostatistics 1 - Fundamentals|
|Teaching period||1st Non standard period (3 May 2021 - 23 June 2021) (Distance learning)|
|Domestic Tuition Fees (NZD)||$1,444.50|
|International Tuition Fees (NZD)||$5,337.00|
- HASC 413
- Limited to
- MA, MAppSc, MClinPharm, MHealSc, MMLSc, MPH, MPharm, MPHC, MSc, DPH, PGDipAppSc, PGDipArts, PGDipHealSc, PGDipMLSc, PGDipPharm, PGDipSci, PGCertPH
- (i) PGCertPHC and PGDipPHC students require approval from the Board of Studies in Primary Health Care to enrol for this paper. (ii) This paper runs for the first half of first semester. (iii) Please note that from 2019, this paper will be offered in the second half of the first semester.
- Students who have completed an undergraduate degree in any discipline or recognised equivalent
Department of Preventive and Social Medicine, Dunedin campus: firstname.lastname@example.org
- More information link
- View more information on postgraduate studies in Public Health
- Teaching staff
- Paper Convenor: Dr Josie Athens
- Paper Structure
- Introduction to Biostatistics
- Descriptive Statistics
- Introduction to Statistical Inference
- Continuous Outcomes
- Binary Outcomes
- Introduction to Study Design
- Participation and contribution: 10% of the marks for this paper will derive from your contribution to Zoom sessions and discussion forums. The marks will not be awarded for the correctness of your contributions, but for making an effort to engage with the question at hand and to use the reading and other learning that you have done to progress the discussion.
- Assignment 1: This assignment, worth 40% of the mark for the paper, assesses your ability to perform descriptive statistics and to report summaries on high-quality tables and plots. The second component of this assignment will focus on your ability to estimate confidence intervals and understand concepts regarding statistical inference.
- Assignment 2: This assignment, worth 50% of the mark for the paper, will focus on your ability to analyse both continuous and binary outcomes and to perform basic sample size and power calculations.
- Teaching Arrangements
- Compulsory webinar sessions: Tuesday afternoons, 4pm-6pm.
- Block Week (zoom webinars): Tuesday 27th April, Wednesday 28th April, Thursday 29 April, 4pm - 6pm
1. Kirkwood, Betty R., and Jonathan AC Sterne. 2003. Essential Medical Statistics. Second Edition. Blackwell.
2. Dalgaard, Peter. 2008. Introductory Statistics with R. Second Edition. Springer.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Critical thinking,
Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes.
- Learning Outcomes
- Students who successfully complete the paper will be able to
- Demonstrate an understanding of types of data and appropriate descriptive statistics and graphical summaries
- Apply skills in simple data analysis methods and measures of precision and interpreting the results
- Demonstrate and apply understanding of the statistical issues in research design and data analysis