![A male and female student chatting over a computer.](http://contentlibrary.manchester.ac.uk/_coursemarketing/UMANC/2024/362/18103/U_development-studies-1400x450.jpg)
- UCAS course code
- LL14
- UCAS institution code
- M20
BAEcon Development Studies and Data Analytics / Course details
Year of entry: 2024
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Course unit details:
Applied Statistics for Social Scientists
Unit code | SOST10142 |
---|---|
Credit rating | 20 |
Unit level | Level 1 |
Teaching period(s) | Semester 2 |
Available as a free choice unit? | Yes |
Overview
The main topics to be covered are:
- Data exploration and visualization using R
- Descriptive statistics
- Modelling with Continuous Data
- Modelling with categorical Variables
Aims
The aims of this course are for each student to achieve:
- an introductory to data cleaning and exploration
- an understanding of basic statistics tests
- an understanding of multivariate statistical analysis
- an ability to use statistical software R
Learning outcomes
- be able to import and export data in R
- be able to prepare data in R
- be able to produce visualisations of data
- be able to understand and run descriptive statistics in R
- be able to understand and run a battery of test of hypothesis in R
- be able to understand and run a variety of statistical models in R
Teaching and learning methods
Lectures, practicals and coursework.
Please note the information in scheduled activity hours are for guidance only and may change.
Assessment methods
Method | Weight |
---|---|
Written assignment (inc essay) | 75% |
Set exercise | 25% |
Feedback methods
Feed-back on individual based essay
Recommended reading
Agresti, A. (2018). Statistical Methods for the Social Sciences (5th ed.). Pearson.
Fogarty, B. (2019). Quantitative Social Science Data with R.
Hothorn, T., & Everitt, B. (2014). A handbook of statistical analyses using R (Third edition). CRC Press, Taylor & Francis Group. Landers, R. N. (2019). A step-by-step introduction to statistics for business. SAGE.
Leon-Guerrero, A., & Frankfort-Nachmias, C. (2018). Essentials of social statistics for a diverse society (Third edition). SAGE.
Wickham, H., & Grolemund, G. (2017). R for Data Science: import, tidy, transform, visualize, and model data. O’Reilly UK Ltd.
Study hours
Scheduled activity hours | |
---|---|
Lectures | 20 |
Practical classes & workshops | 8 |
Independent study hours | |
---|---|
Independent study | 172 |
Teaching staff
Staff member | Role |
---|---|
Todd Hartman | Unit coordinator |