Statistics and Research Methods A
Unit code: HMA103
|Credit points||12.5 Credit Points|
|Contact hours||36 hours per semester|
Note: students may only receive credit for one of: HMA103, HMA104, HMB110, HMB111 and HMS102.
Aims and objectivesThis unit of study is designed to enable students to develop the capacity to carry out independent statistical investigation, together with an awareness of the assumptions and limitations involved with the generalisation of the results of such investigations.
Generic skills outcomes
This unit of study will contribute to helping students achieve some of the attributes expected of Swinburne graduates. The material chosen for this subject reflects the statistical and quantitative knowledge and skills expected in your chosen profession, and will be linked as far as possible, through choices of examples and problems, with current professional practice.
The graduate attributes which relate to this unit help to produce students who:
- Are capable in their chosen professional areas: Students will attain mathematical and statistical knowledge and skills that will support their professional work. This will include abilities in critical enquiry, an awareness of the relationship between statistical theory and practice and an appreciation of the limitations of statistical models.
- Are adaptable and manage change: Problem-solving and research skills are parts of statistical abilities and enable students to investigate problems and issues of their own devising as well as those covered in this unit.
- Are aware of environments: Using appropriate technology will be an important part of this unit and will assist students to develop a socially responsible awareness of the role of technology in society. The development of statistical and research skills will contribute to students being able to evaluate the impact of their professional decisions that have economic, social or environmental implications.
- Ordering & grouping data: frequency tables; picturing data: histograms and stemplots; summarising data: median, IQR & boxplots; the mean & standard deviation; levels of measurement.
- Describing and displaying relationships; Pearson's r; introduction to regression; relationships in tabulated data; correlation and causality.
- Producing data; experiments; population and samples; density curves and normal distribution; the standard normal.
- Making decisions about means, the z and t tests; testing relationships; Pearson's r and the chi-squared test of independence, using SPSS.
- Introduction to estimation, confidence intervals for the mean.