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Unit information: Statistics 1 in 2018/19

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Statistics 1
Unit code MATH11400
Credit points 10
Level of study C/4
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Andrieu
Open unit status Not open

Probability 1 (MATH 11300)


Analysis 1 (MATH11006) and Calculus 1 (MATH11007), or equivalent, are normally required but may be taken concurrently.

School/department School of Mathematics
Faculty Faculty of Science


Unit aims

To introduce the role of statistics in contemporary applications and to develop an elementary understanding of, and fluency in, the statistical paradigm of data collection, exploration, modelling and inference.

General Description of the Unit

Computer technology has revolutionised both the scope and method of statistics, and this unit aims to give a basic grounding in statistical methodology that reflects this contemporary view. The role of statistics in the modern world is becoming ever-wider and applications can be found in almost all fields of human endeavour - in science, medicine, industry, social science, commerce and government. Taking real-life examples as motivation, this unit aims to develop an understanding of the basic principles of statistics, combining exploratory techniques and the machinery of probability theory to build a toolkit that can be used to uncover and identify relationships in the presence of random variation.

Relation to Other Units

This unit is part of the foundation for all statistics units in later years.

Additional unit information can be found at

Intended learning outcomes

Students should be able to:

  • Use exploratory techniques to identify simple relationships in data;
  • Formulate simple statistical models as appropriate to particular applications;
  • Understand the principles of parametric modelling, and be able to derive parameter estimates for simple models using method-of-moments and maximum likelihood;
  • Derive the simple linear regression model and implement it in appropriate situations;
  • Simulate samples from specified distributions and understand why simulation techniques are a useful statistical tool;
  • Use simulation techniques to explore sample variation;
  • Calculate and understand confidence intervals for simple models by both exact and simulation methods;
  • Formulate and carry out hypothesis tests by exact and simulation methods;
  • Use the statistical software system R to support each of the above tasks.

Transferable Skills

Use of statistical software for elementary statistical analysis on the computer.

Teaching details

Lectures supplemented by weekly small group tutorials for first year students. Weekly problem sheets, with outline solutions available a fortnight later.

Assessment Details

100% Examination

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.

If you fail this unit and are required to resit, reassessment is by a written examination in the August/September Resit and Supplementary exam period.

Reading and References

Reading and references are available at