ST952 An Introduction to Statistical Practice
Please note that all lectures for Statistics modules taught in the 2022-23 academic year will be delivered on campus, and that the information below relates only to the hybrid teaching methods utilised in 2021-22 as a response to Coronavirus. We will update the Additional Information (linked on the right side of this page) prior to the start of the 2022/23 academic year.
Throughout the 2021-22 academic year, we will be adapting the way we teach and assess your modules in line with government guidance on social distancing and other protective measures in response to Coronavirus. Teaching will vary between online and on-campus delivery through the year, and you should read the additional information linked on the right hand side of this page for details of how this will work for this module. The contact hours shown in the module information below are superseded by the additional information.You can find out more about the University’s overall response to Coronavirus at: https://warwick.ac.uk/coronavirus.
All dates for assessments for Statistics modules, including coursework and examinations, can be found in the Statistics Assessment Handbook at http://go.warwick.ac.uk/STassessmenthandbook
ST952-15 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 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 (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 C3
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Assignment 1 & 2 | 50% | 72 hours | Yes (extension) |
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Due in Term 1 Week 6. |
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| In-person Examination | 50% | 2 hours | No |
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The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade.
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Feedback on assessment
Feedback for reports will be available within 20 working days.
Cohort level feedback and solutions will be provided for the examination.
Post-requisite modules
If you pass this module, you can take:
- ST409-15 Medical Statistics with Advanced Topics
Courses
This module is Core for:
- Year 1 of TSTA-G4P1 Postgraduate Taught Statistics