| Starting | Week | Topic | Practical Skills | Assessment |
|---|---|---|---|---|
| February 23 | 1 | Basic Statistical Concepts, Introduction to R Programming, Data Wrangling with R | Basic R Programming, Data Wrangling with R | |
| March 02 | 2 | Data Visualisation with R, Statistical Communication and Workflow | Data Visualisation with R, Literate Programming | |
| March 09 | 3 | Probability | Data Organisation | Quiz 1 due |
| March 16 | 4 | Discrete Random Variables | Data Manipulation | |
| March 23 | 5 | Effective Communication of Statistics | Quiz 2 due | |
| March 30 | 6 | Continuous Random Variables | Advanced R Programming | In-Tutorial Data Analysis Task |
| Midsemester Break (2 weeks) | ||||
| April 20 | 7 | Sampling Distribution, Point and Interval Estimators | Quiz 3 due | |
| April 27 | 8 | Hypothesis Testing: Single Population | ||
| May 04 | 9 | Hypothesis Testing: Comparing Two Populations | Quiz 4 due | |
| May 11 | 10 | Simple Linear Regression, Multiple Linear Regression | ||
| May 18 | 11 | Multiple Linear Regression | Quiz 5 due | |
| May 25 | 12 | ANOVA, Chi-squared Tests | Assignment due | |
STAT1003 – Statistical Techniques
This course introduces students to the philosophy and methods of modern statistical data analysis and inference, with a particular focus on applications to the life sciences. The course has a strong emphasis on computing and graphical methods, and uses a variety of real-world problems to motivate the theory and methods required for carrying out statistical data analysis. This course makes extensive use of R statistical analysis package interfaced through R Studio.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Summarise and graph data appropriately;
- Work with random variables and probability distributions and describe the rationale behind them;
- Describe and use the normal distribution appropriately;
- Identify when and how to carry out basic statistical inference including confidence intervals, hypothesis testing, regression and analysis of variance; and,
- Identify contexts in which particular statistical methods may be inappropriate.
Requirement
We will be making heavy use of the R language and the RStudio Desktop (or optionally, you may use Positron). If you are using your own computer or laptop, please ensure you have R version 4.5.0 or greater and RStudio Desktop version 2026.01 or later (or Positron).
Schedule
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