Introductory description

This module runs in Term 1 and is core for students on an MSc in Statistics. It is not available for undergraduate students.

Module web page

Module aims

Students on the Diploma and MSc often had diverse academic backgrounds. This course complements ST903 Statistical Methods in giving a common starting point to the programme, with an emphasis on learning skills in practical statistics.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Exploratory data analysis (numerical and graphical measures)
A hands-on introduction to R, exercises to learn basics of R.
Simpson's paradox, Regression to the mean, Correlation vs causation
Simple linear regression; Correlation coefficient, SD line, Regression Line
Multiple linear regression; Diagnostic plots, Hypothesis testing, ANOVA
Structured Data (coming from simple experimental designs)
Generalised Linear Models; Poisson and Binomial data
Contingency tables and non-parametric tests

Learning outcomes

By the end of the module, students should be able to:

Computational skills: Basic use of R, search for commands in help files and understand them, dealing with data (collecting, typing in, downloading, storing, sharing etc.)
Descriptive statistics and Explorative Data Analysis (EDA): Data structures, appropriateness of data (relevance to the scientific question(S), completeness, quality etc.), representation of data (choice of the form, optimal layout, misleading representation etc.), strategies to explore certain aspects of the data
Modelling and analysis: choice of model, discussion of model assumptions, fitting models, validation and comparison of models, prediction, sensitivity analysis (in respect to assumptions and sample data), simulation
Context: translating scientific queries into statistical questions, classification of investigations, drawing scientific conclusions from statistical analysis
Communication skills: listening, asking questions, explaining analysis, approach and delivering results to a non-statistician, writing a report

Indicative reading list

View reading list on Talis Aspire

Subject specific skills

-Data structures, appropriateness of data (relevance to the scientific question(s), completeness, quality etc.), representation of data (choice of the form, optimal layout, misleading representation etc.), strategies to explore certain aspects of the data
-choice of model, discussion of model assumptions, fitting models, validation and comparison of models, prediction, sensitivity analysis (in respect to assumptions and sample data), simulation
-translating scientific queries into statistical questions, classification of investigations, drawing scientific conclusions from statistical analysis

Transferable skills

-Basic use of R, search for commands in help files and understand them, dealing with data (collecting, typing in, downloading, storing, sharing etc.)
-listening, asking questions, explaining analysis approach and delivering results to a non-statistician, writing a report.

Study time

Type	Required
Lectures	20 sessions of 1 hour (26%)
Practical classes	10 sessions of 2 hours (26%)
Private study	36 hours (47%)
Total	76 hours

Private study description

Weekly revision of lecture notes and materials, wider reading, practice exercises, learning to code in R and preparing for examination.

Costs

No further costs have been identified for this module.

You must pass all assessment components to pass the module.

Students can register for this module without taking any assessment.

Assessment group C3

	Weighting	Study time	Eligible for self-certification
Assessment component
Assignment 1 & 2	50%	72 hours	Yes (extension)
Due in Term 1 Week 6. You will work as part of a small group to carry out analysis of a dataset and provide a written report in response to a set of prompt questions. 500 words is equivalent to one page of text, diagrams, formula or equations; your ST952 Assignment 1 should not exceed 8 pages in length. Due in Term 1 Week 10. You will work as part of a small group to carry out analysis of a dataset and provide a written report in response to a set of prompt questions. 500 words is equivalent to one page of text, diagrams, formula or equations; your ST952 Assignment 2 should not exceed 8 pages in length.
Reassessment component
Reassessment Assignment			Yes (extension)
You will be asked to complete this project if you failed the module and if the coursework in the original assessment, i.e. if your combined mark for assignment 1 and assignment 2 was lower than 50% You will be given one or two datasets and asked to analyse and fit a variety of statistical models to these and assess the results. You will be asked to write-up your findings in a suitable report. These will be similar in nature to the original two assignments but on an individual basis. 500 words is equivalent to one page of text, diagrams, formula or equations; your ST952 Data analysis Problems project should not exceed 15 pages in length.
Assessment component
In-person Examination	50%	2 hours	No
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade. ,Moodle Answerbook Pink (12 page) Students may use a calculator Cambridge Statistical Tables (blue)
Reassessment component
In-person Examination - Resit			No
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade. ~Platforms - AEP,Moodle Answerbook Pink (12 page) Students may use a calculator Cambridge Statistical Tables (blue)

Feedback on assessment

Feedback for reports will be available within 20 working days.

Cohort level feedback and solutions will be provided for the examination.

Past exam papers for ST952

Courses

This module is Core for:

Year 1 of TSTA-G4P1 Postgraduate Taught Statistics

ST952-15 An Introduction to Statistical Practice