| Semester 1, 2022 Online | |
| Units : | 1 |
| Faculty or Section : | Faculty of Health, Engineering and Sciences |
| School or Department : | School of Mathematics, Physics & Computing |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Examiner:
Requisites
Enrolment is not permitted in STA8170 if STA2300 or STA1003 has been previously completed.
Overview
This course aims to provide post graduate students with no or limited knowledge of statistics, the fundamental statistical concepts, methods and data analysis skills necessary to undertake quantitative research and interpret subsequent results. The course is aimed at developing statistical thinking and literacy for students from diverse disciplines, and is a pre-requisite for most high-level statistics courses.
This course focuses primarily on the graphical and numerical methods of data analysis including descriptive and inferential statistics for post-graduate students. Emphasis is on the understanding of the concepts and principles associated with statistical methods and skills required for the analysis of real-life for quantitative studies. Core components of the course include the use of a popular statistical software package (SPSS) and the development of problem-solving skills relevant to many disciplines. However, the mathematical underpinnings of the methods are not covered in detail in this course; other statistics courses cover this aspect.
Course learning outcomes
On Completion of this course students should be able to
- Recognise types of data and describe them using appropriate graphical and numerical analyses including exploring relationships in data;
- Distinguish between different methods of data generation, collection and analysis;
- Select and implement appropriate statistical methods to analyse quantitative data to answer an underlying research question;
- Prepare, manage, format and analyse data using a popular statistical computer package in order to apply appropriate statistical inferencing procedure;
- Apply professional skills to effectively present and communicate results of statistical analyses including interpretation and justification of statistical decisions to a diverse scholarly audience.
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. | Exploring and understanding data: variables and values; types of data; introduction to SPSS; Describing distributions: quantitative data; graphs of distributions; summary statistics. | 8.00 |
| 2. |
Using the normal model: standardising; unstandardising; standard normal curve; using Table Z. |
8.00 |
| 3. | Exploring relationships between variables: scatterplots; correlation and regression; residuals. | 8.00 |
| 4. | Gathering data: Observational and experimental studies; surveys; sampling methods; principles of good design; causation and confounding. | 8.00 |
| 5. | Randomness and probability: probability rules; events; probability models; means and standard deviation; the binomial model. | 10.00 |
| 6. | Sampling distribution models: proportions and means; standard error; the central limit theorem. | 8.00 |
| 7. | Generalising to the World at Large: introduction to hypothesis testing and confidence intervals; the z test for proportion; Type I and Type II errors; P-value; significance level, power of test; sample size determination. | 10.00 |
| 8. | Hypothesis testing and confidence interval for one mean; one sample t-procedure for a mean; and dependent/paired samples t procedure; confidence intervals; determination of sample size; use of t table. | 10.00 |
| 9. | Comparing means: two sample t-procedures; confidence intervals and hypothesis testing. One-way Analysis of Variance (ANOVA); pairwise comparison; use of F table. | 10.00 |
| 10. | Linear Regression Analysis: Inference for simple regression model; introduction and inference for multiple regression model 鈥� test and confidence interval for regression parameters. | 10.00 |
| 11. | Categorical variables; joint, marginal and conditional distributions; Chi-square test of independence; follow-up residual analysis; use of Chi-square table. | 10.00 |
Text and materials required to be purchased or accessed
Otherwise:
1) students can access SPSS free online through using their USQ credentials to login (more information will be provided on the course 精东传媒app Desk)
2) a one year student license can be purchased on disk from the USQ Bookshop, or a six-month license for a download version can be purchased online from the Australia and New Zealand distributors of SPSS, Hearne Software. Before deciding to choose the download option check that the size of the download is compatible with your internet quota and speed. Be aware that the SPSS student license only allows limited reinstalment within the designated license period from initial installation).
Student workload expectations
To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.
Assessment details
| Description | Group Assessment |
Weighting (%) | Course learning outcomes |
|---|---|---|---|
| Quiz | No | 10 | 1,4 |
| Problem Solving 1 | No | 25 | 1,2,3,4,5 |
| Problem Solving 2 | No | 25 | 1,2,3,4,5 |
| Report | No | 40 | 2,3,5 |