Undergraduate Course: Statistics (Yr 3) (MATH09022)
|School||School of Mathematics
||College||College of Science and Engineering
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 9 (Year 3 Undergraduate)
|Home subject area||Mathematics
||Other subject area||None
||Taught in Gaelic?||No
|Course description||Syllabus summary: Summary statistics, sampling distributions, hypothesis testing, interval estimation, likelihood, analysis of categorical data, joint, marginal and conditional distributions, ANOVA and regression. The computer program R will be introduced.
Information for Visiting Students
|Displayed in Visiting Students Prospectus?||No
Course Delivery Information
|Delivery period: 2013/14 Semester 2, Available to all students (SV1)
||Learn enabled: Yes
|Course Start Date
|Breakdown of Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Breakdown of Assessment Methods (Further Info)
|Main Exam Diet S2 (April/May)||MATH09022 Statistics (Year 3)||2:00|
|Resit Exam Diet (August)||MATH09022 Statistics (Year 3)||2:00|
Summary of Intended Learning Outcomes
|- Knowledge of common statistical procedures, and their implementation in a statistical package.
- Understanding of randomness and, in particular, sampling distributions.
- Ability to conduct simple inferential procedures and to exercise diagnostic and interpretative skills.
- Ability to interpret likelihood analyses.
- Facility with bivariate, marginal and conditional distributions.
- Ability to fit, criticise and predict from simple linear regression and one-way classification models.
- Ability to interpret test statistics and significance probabilities.
- Facility with the R statistical package for methods of inference developed in the course.
|See 'Breakdown of Assessment Methods' and 'Additional Notes' above.|
||Summary statistics, sampling distributions, hypothesis testing, interval estimation, likelihood, analysis of categorical data, joint, marginal and conditional distributions, ANOVA and regression. The computer program R will be introduced through a two-hour practical near the beginning of the course. Its use will be supported with examples in lectures and tutorials with supplementary material on the course website.
||See 'Breakdown of Learning and Teaching activities' above.
|Course organiser||Prof Colin Aitken
Tel: (0131 6)50 4877
|Course secretary||Mrs Kathryn Mcphail
Tel: (0131 6)50 4885
© Copyright 2013 The University of Edinburgh - 10 October 2013 4:51 am