This module is designed to:

1. Provide students with an insight into the process of scientific research;
2. Expose students to the research environment;
3. Develop in students the confidence to work independently, to effectively and efficiently attain a scientific goal;
4. Develop in students the ability to communicate scientific concepts and findings in a variety of formats.

Use appropriate statistical approaches to test hypotheses.

Tutorial - This tutorial introduces the module to the student cohort

Introduction to directed self-learning in advanced statistics and the learning environment. Registration for this module requires you have to previously and successfully completed an undergraduate statistics module at Liverpool or elsewhere. Special arrangements for students off campus.

Electure - For each of the 9 topics covered in the module, there is an electure provided on Vital. This gives guidance on the recommended topic reading, the underlying theory and practical examples of the statistical topic in question.

Tutorial - Skype based tutorials are held each week to accompany each electure. These provide one to one teaching and feedback from academic staff.

Students should attempt to complete the activities highlighted in each electure before attending the tutorial. Difficulties in understanding can then be discussed in the tutorial.

Each topic will follow a similar format, consisting of four main sections:

a. Introduction to the topic and relevant theory

b. Topic-specific examples and explanations of typical statistical output

c. Step-by-step instructions on utilisation in R

d. Student conducts their own analysis of data and reports the results

Topic 1 Introduction to how the module runs, a review of inference tests and how to set up R and read in data. By the end of this topic you should be clear about how to approach your learning, the learning timetable and framework, refreshed your basic statistical knowledge regarding elementary types of statistical test and statistical inference, and grasped the essentials of the R environment.

Topic 2 The R environment  and complete elementary statistical tests.

Topic 3 Linear and multiple regression. In this topic we revisit a topic that should be reasonably familiar to you and we explore how to test for significance, to test the assumptions underlying the analyses and how to draw the correct conclusions.

Topic 4 Analysis of variance. The analysis of designed experiments is a mainstay in all biological experiments. Assessing  the relationship between treatment factors that may or may not significantly influence the response variable depends on the design of the experiment and how you replicated measurements. In this topic we look at ''how to do'' analyses of variance.

Topic 5 Extending the linear model. Generalized linear modelling (GLM) is an important core concept in statistics. In this topic you will learn how to further analyse data by multiple regression where error distributions are both normally distributed and where they are not.

Topic 6 Model selection. Here we go into greater depth illustrating how you choose and assess what is the ''best'' model to statistically describe a set of data.

Topic 7 Non-linear relationships. In this topic we consider issues surrounding the statistics of what is colloquially known as ''curve fitting'' where the response variable is continuous. In particular, we consider the rectangular hyperbola from the perspective of enzyme kinetics.

T opic 8 Probits, logits, logistic regression and dose response analysis. Picking up on topics 5, 6 and 7 here we look further at the application of GLMs to analysing survivorship, proportional data and what are commonly known as dose response curves.

Topic 9 Genomic analysis.R is particularly powerful for analysing large data sets. In this final topic we explore approaches used to analyse genomic data (micro-array, RNA-seq) in genomics studies.

Topic 10 Assessment 3. The final assessment for this module occurs in this workshop.