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

The details
2020/21
Mathematical Sciences
Colchester Campus
Spring
Undergraduate: Level 4
Current
Monday 18 January 2021
Friday 26 March 2021
15
10 September 2020

 

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

 

(none)

Key module for

(none)

Module description

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.

Module aims

The purpose of this module is to introduce:

• Data analysis.
• Statistics.
• The use of R for data analysis and statistics.

Module learning outcomes

A. The use of R for carrying out statistical analysis
B. Data analysis techniques
C. Statistical techniques

Module information

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

Learning and teaching methods

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.

Bibliography

(none)

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Final Project & Presentation     

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Andrew Harrison, email: harry@essex.ac.uk.
Dr Andrew Harrison, Dr Osama Mahmoud & Dr Mario Gutierrez-Roig
Dr Andrew Harrison (harry@essex.ac.uk), Dr Osame Mahmoud (o.mahmoud@essex.ac.uk), Dr Mario Gutierrez-Roig (mario.gutierrez-roig@essex.ac.uk)

 

Availability
Yes
Yes
Yes

External examiner

No external examiner information available for this module.
Resources
Available via Moodle
Of 20 hours, 10 (50%) hours available to students:
10 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information
Mathematical Sciences

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