Overview
Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count data, analysis of variance and experimental design. Sampling and design principles of techniques to build on in the implementation of research studies.
This is a paper in statistical methods for students from any of the sciences, including students studying biological sciences, social sciences or sport science, as well as those studying mathematics and statistics. The paper provides an introduction to the use of statistical methods for the description and analysis of data, use of computer software to carry out data analysis, and the interpretation of the results of statistical analyses for a range of research studies.
About this paper
| Paper title | Statistical Methods |
|---|---|
| Subject | Statistics |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period(s) | Summer School
(On campus)
Semester 1 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,040.70 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Restriction
- STAT 115, (BSNS 102 or BSNS 112), QUAN 101
- Schedule C
- Arts and Music, Science
- Eligibility
Suitable for students of all disciplines with an interest in the quantitative analysis of data. There are no formal mathematical or statistical prerequisites for this paper, but students who have not done mathematics or statistics at NCEA Level 3 are encouraged to make use of the online and tutorial resources available as part of the paper.
- Contact
- Teaching staff
Summer School Course Co-ordinator: Dr Tilman Davies
Semester 1 Course Co-ordinator: Associate Professor Matthew Schofield
- Paper Structure
The paper covers:
- Data: where does it come from and how should we collect it?
- Probability
- Binomial and normal distributions
- Sampling distributions
- Confidence Intervals and estimation
- Hypothesis testing and power
- Contingency tables for categorical data
- Analysis of variance
- Regression including simple linear and multiple linear with confounding discussion
- Teaching Arrangements
Four 1-hour lectures per week, plus cafeteria-style (voluntary attendance) tutorials each week for assistance with course material and exercises.
- Textbooks
There is no set text. Copies of the lecture slides will be made available during the course.
- Graduate Attributes Emphasised
Interdisciplinary perspective, Scholarship, Communication, Critical thinking, Information literacy.
View more information about Otago's graduate attributes.- Learning Outcomes
By the end of the course students should:
• Be aware of the appropriate use of common study designs
• Be able to describe the information contained in a data set
• Be able to carry out common statistical data analyses in R
• Be able to interpret the results of common statistical analyses