MA334-4-SP-CO:
Data analysis and statistics with R

The details
2023/24
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Spring
Undergraduate: Level 4
Current
Monday 15 January 2024
Friday 22 March 2024
15
08 January 2024

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

BA Q120 Linguistics with Data Science,
BA Q121 Linguistics with Data Science (Including Foundation Year),
BA Q122 Linguistics with Data Science (Including Placement Year),
BA Q123 Linguistics with Data Science (Including Year Abroad)

Module description

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.

Module aims

The aims of this module are:



  • To introduce data analysis.

  • To introduce statistics.

  • To introduce the use of R for data analysis and statistics.

Module learning outcomes

By the end of this module, students will be expected to be able to:



  1. Use R to implement the statistical methods introduced in the Module.

  2. Understand the basis for the statistical methods introduced in the Module and recognise situations where they apply.

  3. 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.

Module information

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.

Learning and teaching methods

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.

Bibliography

This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Final Project  26/04/2024   

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

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Lisa Voigt, email: lv18675@essex.ac.uk.
Dr Lisa Voigt; Dr Dan Brawn; Dr Hirbod Assa
lv18675@essex.ac.uk

 

Availability
Yes
Yes
Yes

External examiner

Dr Yinghui Wei
University of Plymouth
Resources
Available via Moodle
Of 49.8 hours, 47.9 (96.1%) hours available to students:
2 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information

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