MA334-4-SP-CO:
Data analysis and statistics with R
2021/22
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Spring
Undergraduate: Level 4
Current
Monday 17 January 2022
Friday 25 March 2022
15
28 October 2021
Requisites for this module
(none)
(none)
(none)
(none)
(none)
BSC 5B43 Statistics (Including Year Abroad),
BSC 9K12 Statistics,
BSC 9K13 Statistics (Including Placement Year),
BSC 9K18 Statistics (Including Foundation Year),
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)
The 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 purpose of this module is to introduce:
• Data analysis.
• Statistics.
• The use of R for data analysis and statistics.
A. The use of R for carrying out statistical analysis
B. Data analysis techniques
C. Statistical techniques
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
Using tables of the t, F and chi-squared distributions
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 will be delivered in a way that blends face-to-face classes, for those students that can be present on campus, with a range of online lectures, teaching, learning and collaborative support.
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 & Presentation |
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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 Dan Brawn, email: dbrawn@essex.ac.uk.
Dr Dan Brawn
dbrawn@essex.ac.uk
Yes
Yes
Yes
No external examiner information available for this module.
Available via Moodle
Of 1722 hours, 30 (1.7%) hours available to students:
1692 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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