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Course
Statistics for Computer Science FC0002
Module information>
You will master the fundamental principles of probability theory and gain an understanding of statistical inference methods, particularly concerning common measures like means and proportions. By the end of the module you will learn how to use simple causal models and know when it is appropriate to do so, and apply a variety of methods for explaining, summarising and presenting data and interpreting results clearly.
About this module
This course is at an elementary mathematical level, and introduces the ideas of probability and statistical inference which may be further develop during undergraduate study.
The major aims of this module are for students to master the fundamental principles of probability theory, enabling the understanding and application of various statistical operators and recalling essential probability distributions and their properties; to acquire competence in statistical inference methods, particularly concerning common measures like means and proportions, for making informed decisions based on data analysis; to cultivate the ability to construct and use simple causal models, discerning their appropriateness in different contexts, thereby fostering a deeper understanding of causality in statistical analysis; finally, to establish a strong foundational knowledge in statistics essential for subsequent STEMM modules, laying the groundwork for further statistical analysis at undergraduate level.
Topics covered
- Descriptive statistics: measures of central tendency and dispersion
- Graphical displays of univariate data
- Comparing distributions and exploring bivariate data
- Probability fundamentals
- Random variables and their properties
- Common discrete probability distributions
- Common continuous probability distributions
- Sampling distributions of statistics
- Interval estimation: one population
- Interval estimation: two populations
- Hypothesis testing principles
- Hypothesis testing: one population
- Hypothesis testing: two populations
- Contingency tables and the chi-squared test
- Correlation coefficients
- Simple linear regression
Learning outcomes
If you complete the module successfully, you will be able to:
- apply a variety of methods for explaining, summarising and presenting data and interpreting results clearly
- apply and be competent users of standard statistical operators and be able to recall a variety of well-known distributions and their statistical properties
- perform statistical inference related to common measures such as means and proportions
- use simple causal models and know when it is appropriate to do so. statements involving logical symbols and set notation.
Assessment
Unseen written exam (Two-hour 15 minutes).
