MATH20812 - 2009/2010
General Information
- Title: Practical Statistics 1
- Unit code: MATH20812
- Credit rating: 10
- Level: 2
- Pre-requisite units: MATH10141, MATH20701
- Co-requisite units: MATH20802 Statistical Methods
- School responsible: Mathematics
- Members of staff responsible: Dr. Georgi Boshnakov
Unit specification
Aims
The programme unit aims to introduce statistical concepts and techniques, including hypothesis testing, regression, analysis of variance and simulation, to students specialising in statistics. To provide experience with the statistical system Minitab.
Brief description
In this course statistical methods and concepts are put in the context of their practical application with emphasis on model selection and diagnostics. Students do a series of small projects. Each project is a complete data analysis exercise centred around some statistical topic.
Intended learning outcomes
On completion of this unit successful students will:
- be familiar with essential ideas and techniques in statistics;
- be able to choose appropriate methods to tackle a variety of data analysis problems;
- have a working knowledge of the statistical systems Minitab and R and be able to use at least one of them confidently;
- have gained experience in the writing of statistical reports;
- be able to set up simple simulation experiments.
Future topics requiring this course unit
- The knowledge and skills developed by this course are essential for anybody who applies statistical methods and will be useful for most statistics courses in later years.
- The ideas in this course unit are taken further in Practical Statistics 2 (level 3 semester 2).
Syllabus
- Overview of the statistical system. Probability, probability density and expectation. Exploring distributions of data: descriptive statistics and graphical methods. Fitting distributions to data. Goodness-of-fit tests. Probability plots. [6]
- Confidence Intervals. Comparisons using confidence intervals. [4]
- Significance tests. One- and two-sample t-tests. [4]
- Multiple comparisons and one-way analysis of variance. [2]
- Regression (inference). [2]
- Regression (model selection). [2]
- Jacknife and bootstrap. [2]
- A.H. Kvanli et al., Introduction to Business Statistics, 4th, 5th, and 6th Edition, St. Paul, Minneapolis: West Publishing Company, 1996-2003.
- R.E. Walpole and R.H. Myers, Probability and Statistics for Engineers and Scientists, 6th Edition, London: Prentice Hall, 1998.
- Details to be announced
Textbooks
Learning and teaching processes
Two lectures and one laboratory session each week. In addition students should expect to do at least four hours of private study each week on this course unit.