School of Computing and Mathematics

Faculty of Natural Sciences

For academic year: 2022/23 Last Updated: 14 February 2023

MAT-20027 - Linear Statistical Models

None

Level 4 SH/DH Mathematics or equivalent

Level 5 Probability

Level 5 Probability

None

This module presents an introduction to statistical inference and illustrates the theory with practical applications to real life data sets.

The first half of the module will encorporate hypothesis testing, confidence interval and parameter estimation. The second half of the module considers regression and anova methods, and demonstrates how statistical models can be used to draw conclusions from observations and experimental data collected in the physical and social sciences.

This module develops the following Keele Graduate attributes:

1. An open and questioning approach to ideas, demonstrating curiosity and independence of thought.

4. The ability to solve problems creatively using a range of different approaches and techniques, and to determine which techniques are appropriate for the issue at hand.

6. The ability to communicate clearly and effectively in written and verbal form.

The first half of the module will encorporate hypothesis testing, confidence interval and parameter estimation. The second half of the module considers regression and anova methods, and demonstrates how statistical models can be used to draw conclusions from observations and experimental data collected in the physical and social sciences.

This module develops the following Keele Graduate attributes:

1. An open and questioning approach to ideas, demonstrating curiosity and independence of thought.

4. The ability to solve problems creatively using a range of different approaches and techniques, and to determine which techniques are appropriate for the issue at hand.

6. The ability to communicate clearly and effectively in written and verbal form.

To give a formal introduction to statistical inference and the construction of statistical methods, including hypothesis testing and confidence interval estimation.

To provide a theoretical treatment of the linear regression model and of the estimation, model selection and testing procedures required to fit the model to data; practical aspects of fitting the models in standard statistical software (most likely SPSS) will also be covered.

To provide a theoretical treatment of the linear regression model and of the estimation, model selection and testing procedures required to fit the model to data; practical aspects of fitting the models in standard statistical software (most likely SPSS) will also be covered.

understand concepts of statistical inference, calculate confidence and prediction intervals and perform hypothesis tests for model parameters: 1,2

perform hypothesis tests using analysis of variance and assess the lack of fit of a linear regression model: 1,2

perform least squares estimation and understand and derive the properties of the least squares estimators for parameters of the linear regression model: 1,2

report and interpret results, including model selection and model checking, derived from data analysis using standard statistical package(s): 1,2

perform hypothesis tests using analysis of variance and assess the lack of fit of a linear regression model: 1,2

perform least squares estimation and understand and derive the properties of the least squares estimators for parameters of the linear regression model: 1,2

report and interpret results, including model selection and model checking, derived from data analysis using standard statistical package(s): 1,2

24 hours lectures

24 hours tutorial/examples classes

30 hours coursework preparation

70 hours private study

2 hours unseen examination

24 hours tutorial/examples classes

30 hours coursework preparation

70 hours private study

2 hours unseen examination

None

Assignment x 2 given out in class, with one week given for completion of each

There will be two assignments, each carrying 10% weight towards the final module mark. Assignments will be given in teaching week 5 and in teaching week 10. Students will be given 1 week to complete each assignment. The assignments will consist of questions covering both the theoretical and practical aspects of the material covered up to the point when the assignments are handed out.

2-hour unseen examinations

The examination paper will consist of no less than five and not more than eight questions, all of which are compulsory.