- Homepage
- Key Information
- Students
- Taught programmes (UG / PGT)
- Computer Science
- Engineering
- Geology (CSM)
- Mathematics (Exeter)
- Mathematics (Penryn)
- Mining and Minerals Engineering (CSM)
- Physics and Astronomy
- Renewable Energy
- Natural Sciences
- CSM Student and Staff Handbook

- Student Services and Procedures
- Student Support
- Events and Colloquia
- International Students
- Students as Change Agents (SACA)
- Student Staff Liaison Committees (SSLC)
- The Exeter Award
- Peer Support
- Skills Development
- Equality and Diversity
- Athena SWAN
- Outreach
- Living Systems Institute Webpage
- Alumni
- Info points and hubs

- Taught programmes (UG / PGT)
- Staff
- PGR
- Health and Safety
- Computer Support
- National Student Survey (NSS)
- Intranet Help
- College Website

## ECM2710 - Statistical Modelling (2015)

MODULE TITLE | Statistical Modelling | CREDIT VALUE | 15 |
---|---|---|---|

MODULE CODE | ECM2710 | MODULE CONVENER | Prof David B. Stephenson (Coordinator) |

DURATION: TERM | 1 | 2 | 3 |
---|---|---|---|

DURATION: WEEKS | 0 | 11 weeks | 0 |

Number of Students Taking Module (anticipated) | 93 |
---|

This module covers the key concepts in statistical modelling by showing how to develop and test probability models for a single normal response in terms of one or more explanatory variables. The module will cover simple and multiple regression, including polynomial regression and the use of categorical explanatory variables (i.e. factors), so encompassing classical analysis of variance techniques.

Prerequisite module: ECM2709 or equivalent

This module aims to develop understanding and competence in statistical modelling by introducing you to the normal theory linear model from a modern perspective. It will provide you with the ability to formulate and apply these models in a range of practical settings, to carry out associated inference appreciating how this relates to the general likelihood inferential framework, and to perform appropriate model selection and model checking procedures. Use will be made of a suitable statistical computer language for practical work.

On successful completion of this module, **you should be able to**:

**Module Specific Skills and Knowledge:**

1 formulate simple and multiple regression models and analyse their properties, including polynomial regression and models which involve categorical explanatory variables (i.e. factors) and understand how the latter relate to classical analysis of variance techniques;

2 demonstrate an awareness of the range of practical situations where it is, and is not, appropriate to employ normal theory linear models;

3 demonstrate understanding of the theory and practice of estimation and inference for the normal theory linear model and be able to apply this to fit models and carry out model selection and checking procedures in a range of practical situations;

4 carry out data analysis using multiple regression and related models in conjunction with a suitable computer language.

**Discipline Specific Skills and Knowledge:**

5 demonstrate understanding and appreciation of the mathematical modelling of stochastic phenomena and its usefulness;

6 demonstrate sufficient knowledge of fundamental ideas central to modern model-based statistics which are necessary to be able to progress to, and succeed in, further studies in statistical modelling of data and of stochastic modelling more generally.

**Personal and Key Transferable/ Employment Skills and Knowledge:**

7 demonstrate general data analysis skills and communicate associated reasoning and interpretations effectively in writing;

8 use relevant computer software competently;

9 demonstrate appropriate use of learning resources;

10 demonstrate self management and time management skills.

- introduction to statistical modeling of relationships between variables: motivation and examples;

- response and explanatory variables;

- continuous and categorical data and associated considerations;

- basic ideas of the normal theory linear model and of associated concepts of model fitting, fitted values, residuals and goodness of fit;

- model identification: descriptive/exploratory data analysis of relationships between variables;

- summary measures of correlation and association, graphical techniques;

- scatterplots, grouped box plots, scatterplot matrices, scatter plot smoothing;

- the linear model for a single explanatory variable: simple regression;

- model formulation, equivalence of maximum likelihood to least squares, point and interval parameter estimation and hypothesis testing (t-test), prediction from simple regression;

- assessment of model fit, sum-of-squares breakdown, goodness of fit (R-squared and F-test), residual analysis,and influential observations;

- the linear model for multiple continuous explanatory variables multiple regression;

- model formulation in matrix notation, point and interval parameter estimation and hypothesis testing (partial t-tests), prediction from multiple regression, multicollinearity, assessment of model fit, sum-of-squares breakdown, goodness of fit (R-squared and F-test), residual analysis,and influential observations;

- special cases of multiple regression: polynomial regression;

- regression models with categorical explanatory variables: factors and auxiliary/indicator variables, ANOVA and ANCOVA;

- model selection in regression: comparison of models, variable selection and model choice including stepwise procedures;

- going beyond the linear model transformation of variables, variance-stabilising transformation, Box-Cox transformation, weighted regression, robust regression, preview of models for a non-normally distributed response (the generalised linear model) very basic ideas and simple illustration.

Scheduled Learning & Teaching Activities | 33.00 | Guided Independent Study | 117.00 | Placement / Study Abroad | 0.00 |
---|

Category | Hours of study time | Description |

Scheduled learning and teaching activities | 22 | Lectures including examples classes |

Scheduled learning and teaching activities | 11 | Tutorials/computer classes |

Guided independent study | 117 | Private study |

Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
---|---|---|---|

Three examples sheets | Approximately 12 hours each | 1-10 | Oral feedback in weekly tutorial classes |

Coursework | 20 | Written Exams | 80 | Practical Exams | 0 |
---|

Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
---|---|---|---|---|

Written exam – closed book | 80 | 2 hours | 1-3, 5-7 | Oral feedback at request of student |

Coursework | 20 | Approximately 12 hours | 1-10 | Written feedback on script and oral feedback in office hour |

Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-reassessment |
---|---|---|---|

All above | Written exam (100%) | All | August Ref/Def period |

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.

If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

information that you are expected to consult. Further guidance will be provided by the Module Convener

ELE – http://vle.exeter.ac.uk

Reading list for this module:

Type | Author | Title | Edition | Publisher | Year | ISBN | Search |
---|---|---|---|---|---|---|---|

Set | Faraway J J | Linear Models with R | Chapman and Hall/CRC (texts in statistical science) | 2004 | 9781584884255 | [Library] | |

Set | Krzanowski W.J. | An Introduction to Statistical Modelling | Arnold | 1998 | 000-0-340-69185-9 | [Library] | |

Extended | Rice, J A | Mathematical Statistics and Data Analysis | 3rd | Brooks Cole | 2007 | 978-0495118688 | [Library] |

Extended | Draper N.R. & Smith H. | Applied Regression Analysis | 3rd edition | John Wiley & Sons | 1998 | 9780471170822 | [Library] |

CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
---|---|---|---|

PRE-REQUISITE MODULES | ECM2709 |
---|---|

CO-REQUISITE MODULES |

NQF LEVEL (FHEQ) | 5 | AVAILABLE AS DISTANCE LEARNING | No |
---|---|---|---|

ORIGIN DATE | Friday 09 January 2015 | LAST REVISION DATE | Tuesday 03 March 2015 |

KEY WORDS SEARCH | General linear model; regression; model assessment. |
---|