STAT110 Statistical Methods
|Summer School||Also available: First Semester|
- What is the biomass of cockles at Papanui Inlet?
- What do overseas tourists come to New Zealand to see?
- Are Hector and Maui dolphins different species?
- Investigating survival of a rare bird
- Is circumcision protective against HIV/AIDS?
- Are Dunedin residents in favour of the new harbourside stadium?
- Identifying sleep problems at high altitude
- Are there pain effects when removing horns of young cattle?
These questions are typical of those posed by scientists working in the biological sciences, life sciences and social sciences. A grounding of statistics and probability is crucial for both carrying out scientific research and understanding the scientific literature.
This is a paper in statistical methods for students from the sciences, including students studying biological science, social sciences, and sport science as well as those studying mathematics and statistics. The paper covers data, an introduction to probability, binomial and normal distributions, estimation, hypothesis testing, contingency tables for categorical data, simple linear regression, introduction to multiple regression, analysis of variance. At the end of the course you should be able to make use of a wide variety of techniques in the design and analyse your own research studies.
This course is intended for students who need to use statistical methods in the other courses, as well as those interested in specializing in statistics and mathematics.
- Data: where does it come from and how should we collect it?
- 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.
None. Course materials including copies of slides from lectures will be available on the resource page. A book of complete notes is also available for purchase through the University Print Shop located in the Central Library.
Clark, M.J. and Randal, J. R. A First Course in Applied Statistics. Pearson
MacGillivray, H. Utts & Heckard's Mind on Statistics. Cengage Learning.
Multiple copies of both references are in the Science Library on close reserve at the Loans Desk.
To be confirmed
10:00-11.50 Monday to Thursday
12:00, 13:00 and 14:00 Tuesday and Thursday
Tutorials are cafeteria style, in that students are free to attend at any scheduled tutorial time.
There are five assignments during the Summer School. They are available via the resource page at times to be announced. The assignments constitute one third of your total internal assessment mark. There are three mastery tests which must be taken in room 124 (first floor of Science 3 building) under test conditions. The mastery tests constitute two-thirds of your total internal assessment mark. There is no terms requirement for this course, so if you miss an assignment or test, you can still pass the course.
The final exam lasts 3 hours and is entirely multiple choice.
Your final mark F in the paper will be calculated according to this formula:
F = max(E, (6E + A + 2T)/9)
- E is the Exam mark
- A is the Assignments mark
- T is the Tests mark
and all quantities are expressed as percentages.
This means that the internal assessment mark will contribute 1/3 of the total mark if it helps, otherwise the exam will contribute all of the final mark.
Students must abide by the University’s Academic Integrity Policy
Academic integrity means being honest in your studying and assessments. It is the basis for ethical decision-making and behaviour in an academic context. Academic integrity is informed by the values of honesty, trust, responsibility, fairness, respect and courage.
Academic misconduct is seeking to gain for yourself, or assisting another person to gain, an academic advantage by deception or other unfair means. The most common form of academic misconduct is plagiarism.
Academic misconduct in relation to work submitted for assessment (including all course work, tests and examinations) is taken very seriously at the University of Otago.
All students have a responsibility to understand the requirements that apply to particular assessments and also to be aware of acceptable academic practice regarding the use of material prepared by others. Therefore it is important to be familiar with the rules surrounding academic misconduct at the University of Otago; they may be different from the rules in your previous place of study.
Any student involved in academic misconduct, whether intentional or arising through failure to take reasonable care, will be subject to the University’s Student Academic Misconduct Procedures which contain a range of penalties.
If you are ever in doubt concerning what may be acceptable academic practice in relation to assessment, you should clarify the situation with your lecturer before submitting the work or taking the test or examination involved.
Types of academic misconduct are as follows:
The University makes a distinction between unintentional plagiarism (Level One) and intentional plagiarism (Level Two).
- Although not intended, unintentional plagiarism is covered by the Student Academic Misconduct Procedures. It is usually due to lack of care, naivety, and/or to a lack to understanding of acceptable academic behaviour. This kind of plagiarism can be easily avoided.
- Intentional plagiarism is gaining academic advantage by copying or paraphrasing someone elses work and presenting it as your own, or helping someone else copy your work and present it as their own. It also includes self-plagiarism which is when you use your own work in a different paper or programme without indicating the source. Intentional plagiarism is treated very seriously by the University.
Unauthorised Collaboration occurs when you work with, or share work with, others on an assessment which is designed as a task for individuals and in which individual answers are required. This form does not include assessment tasks where students are required or permitted to present their results as collaborative work. Nor does it preclude collaborative effort in research or study for assignments, tests or examinations; but unless it is explicitly stated otherwise, each students answers should be in their own words. If you are not sure if collaboration is allowed, check with your lecturer..
Impersonation is getting someone else to participate in any assessment on your behalf, including having someone else sit any test or examination on your behalf.
Falsiﬁcation is to falsify the results of your research; presenting as true or accurate material that you know to be false or inaccurate.
Use of Unauthorised Materials
Unless expressly permitted, notes, books, calculators, computers or any other material and equipment are not permitted into a test or examination. Make sure you read the examination rules carefully. If you are still not sure what you are allowed to take in, check with your lecturer.
Assisting Others to Commit Academic Misconduct
This includes impersonating another student in a test or examination; writing an assignment for another student; giving answers to another student in a test or examination by any direct or indirect means; and allowing another student to copy answers in a test, examination or any other assessment.
A researcher is interested in whether men are more or less likely than women to exercise. A random sample of 90 men and 80 women are surveyed on whether they exercise or not. Of the 90 men, 74 exercise; of the 80 women, 54 exercise. Is there evidence of a difference in the proportion of men and women who exercise?
A psychologist, assisting with the trialling of road signs, is recording motorists’ recall of information. A random sample of 20 motorists were stopped after travelling 1 km beyond one particular sign. Let X be the random variable for the number of motorists who correctly recall the information. What are the conditions necessary for X to be from the binomial distribution?
Sir Ronald Aylmer Fisher (1890-1962) was one of the founders of modern statistics because of his many important contributions. He worked at the Rothamsted Agricultural Experiment Station in England and made many important contributions to genetics as well as statistics.
Amongst his achievements are: