Advanced Statistical Methods - CIM2203

Assessment 1: Written practice exercise

Feedback to students will be given in tutorials, in-class tasks and practical sessions. Graded exercises will be set and model answers and solutions to problems will be provided.

Written practice exercise

Assessment 2: In-Class Test

The online test will be used to assess all the statistical methods covered in this module and the skills with which they are applied in problem-solving.

In-Class Test

Assessment 1: Written Assignment

A written report on statistical analysis carried out on a provided data set. Guidelines can be provided for the statistical methods to be carried out, e.g. display for correlation investigation, and analysis of variance and, especially, multiple regression. The foregoing suggested guidelines are dependent on, and strongly associated with the data that is at issue, and the objectives of the analysis to be carried out.

Written Assignment

<p>This module enables you to build on your knowledge of basic statistical methods, studying a range of statistical techniques which can be used to help solve real-life problems involving the analysis of data and interpretation of results. You will explore sampling theory and estimation of mean and variance, hypothesis testing, significance testing, measures of independence and goodness of fit. You will also study linear, multiple and stepwise regression, correlation and analysis of variance tests. Many techniques will be developed and applied through the use of a statistical package such as R, or Minitab, etc.</p>

School of Computing and Engineering

<p>https://library.hud.ac.uk/readinglists/CIM2203</p>

Computing and Information Systems

CIM2203 - Advanced Statistical Methods

Advanced Statistical Methods

Use appropriate software to demonstrate and enhance the understanding of statistical techniques and their solving of real-life problems.

Apply statistical methodologies and report clearly on related assumptions and preconditions.

Perform statistical estimation and hypothesis tests in a variety of situations.

Demonstrate a knowledge of the underlying principles of statistical estimation and inference.

Describe the distributional assumptions for data, data properties and also formalisation with potential and practical relevance and realisation.