ST95215 An Introduction to Statistical Practice
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 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 handson 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
 Resampling methods such as the BootStrap
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 nonstatistician, 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 nonstatistician, writing a report.
Study time
Type  Required 

Lectures  20 sessions of 1 hour (13%) 
Practical classes  10 sessions of 2 hours (13%) 
Private study  36 hours (24%) 
Assessment  74 hours (49%) 
Total  150 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 C4
Weighting  Study time  Eligible for selfcertification  

Assignment 1 & 2  50%  72 hours  No 
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. 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. 

Inperson 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.

Feedback on assessment
Feedback for reports will be available within 20 working days.
Cohort level feedback and solutions will be provided for the examination.
Postrequisite modules
If you pass this module, you can take:
 ST40915 Medical Statistics with Advanced Topics
 ST95560 Dissertation
Courses
This module is Core for:
 Year 1 of TSTAG4P1 Postgraduate Taught Statistics