MA334-7-AU-CO:
Data analysis and statistics with R
2023/24
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 05 October 2023
Friday 15 December 2023
15
08 January 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G30512 Applied Data Science,
MSC G30524 Applied Data Science,
MSC G30612 Data Science and its Applications,
MSC G30624 Data Science and its Applications
This module will introduce concepts from data analysis and statistics and show how they can be applied effectively via the R language. It will cover a wide introduction to statistics and provide practical experience of real-world examples of how statistics is used to gain insights.
Throughout these examples, and many more, we will teach programming techniques that will enable students to apply statistical approaches to real-world applications. This module assumes no previous exposure to statistics.
The aims of this module are:
- To introduce data analysis.
- To introduce statistics.
- To introduce the use of R for data analysis and statistics.
By the end of this module, students will be expected to be able to:
- Be able to implement the statistical methods introduced in the module using code.
- Understand the basis for the statistical methods introduced in the Module and recognise situations where they apply.
- Have developed sufficient analytical skills and demonstrate these in their own words in an individually written and assessed analysis of a given substantial data set.
- Be able to demonstrate significant proficiency with independent and critical analytical skills, showing an ability to use the tools provided to extend the data analysis presented in the module material and communicate effectively within a written report.
Syllabus
- Basic ideas of probability and statistical distributions.
- Random variables, means, covariance and variance Variance of a sample mean and confidence intervals for means, variances and differences between means.
- Conditional probability and independence.
- Probability distribution theory.
- Standard distributions and their use in modelling: including Bernoulli, Binomial, Poisson Estimation and Maximum Likelihood estimators.
- Hypothesis tests concerning means and variances.
- Null and alternative hypotheses.
- Type I and type II errors.
- Test statistic and critical region.
- Probability value and level of significance.
- Basic contingency tables and hypothesis tests for independence such as the chi-squared test.
- Introduction to linear regression.
- The least square estimates of the intercept and the slope of a simple linear regression.
- Confidence intervals for the slope parameter and prediction intervals for response.
- Coefficient of determination and the sample correlation coefficient.
Teaching in the School will be delivered using a range of face to face lectures, classes and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Final Project |
|
100% |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Lan Truong, email: lan.truong@essex.ac.uk.
Dr Lan Truong; Dr Dan Brawn
vt23151@essex.ac.uk
Yes
Yes
Yes
Dr Yinghui Wei
University of Plymouth
Available via Moodle
Of 45 hours, 45 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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