https://github.com/3mmarand/bio00058i-qc-skills-2020

University of York, Department of Biology, Stage 2 module: Quantitative and Computational Skills strand of BIO00058I Laboratory and Professional Skills for Bioscientists II

https://github.com/3mmarand/bio00058i-qc-skills-2020

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University of York, Department of Biology, Stage 2 module: Quantitative and Computational Skills strand of BIO00058I Laboratory and Professional Skills for Bioscientists II

https://github.com/3mmaRand/BIO00058I-QC-skills-2020/blob/master/

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5532590.svg)](https://doi.org/10.5281/zenodo.5532590)

![](pics/58I.png)  

# Overview

## Introduction

This strand of 58I is designed to allow you to consolidate and extend your quantitative and computational skills. We build on the experimental design and data analysis skills developed in Stage 1 by deepening your understanding of the concepts underlying regression, t-tests and ANOVA and introducing additional methods of data analysis.

We will not cover methods which are very specific to the Experimental Design / Bioscience Technique option taken. These are are covered in that option by the option leader.

For each topic there will be required independent study followed by a  workshop in which you will work to appropriately analyse, and report on, given data sets. The independent study will involve reading from the course books and the workshop will start with a live demonstration of one of the examples from the independent study

## Assessment
The workshop exercises are formative. You do not need to work on these outside of the workshop itself.

The summative assessment is through the statistical and quantitative problem solving used in the Experimental Design and Bioscience Techniques options you do.

## Topics covered in Computational and Quantitative skills

It would be impossible to cover everything to you might ever need!

Different people will use different methods and tools.

The topics covered have been chosen because they:

* are foundational,
* naturally follow stage 1,
* are widely applicable (in this module and beyond) and
* are transferable conceptually

We will cover:

1. Understanding of General Linear Models (that is, regression, *t*-tests and ANOVA) - Emma Rand

   *T*-tests, ANOVA and regression are used when we have a *continuous* response variable. We revisit these using a linear modelling framework. This means using a single function `lm()` rather than three different ones and enhancing our understanding of the concepts underlying the tests. Learning more about the model assumptions, estimated coefficients and model fit in this familiar context will make it easier to understand them in the new linear modelling context.

2. Generalised Linear Models for Poisson response variables - Emma Rand

   The Generalised Linear Model is an extension of the General Linear model for situations where the response variable does not follow the normal distribution. Key differences between General Linear Models and Generalised Linear Models is that estimates are on a different scale and we use deviance rather than variance for fit.  Here we consider GLMs when your response variable is a count where the coefficients are logged.

3. Generalised Linear Models for Binomial response variables  - Emma Rand

   Here we consider GLMs when your response variable can take only one of two values such as 0 or 1 or dead and alive. These variables follow a binomial distribution. Their estimates are on a "logit" scale.

4. Non-linear Models (non-linear regression) - Jon Pitchford


Quantitative and Computational Skills strand of BIO00058I Laboratory and Professional Skills for Bioscientists II by Emma Rand is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Please cite as:  
Emma Rand. (2021). Quantitative and Computational Skills strand of BIO00058I Laboratory and Professional Skills for Bioscientists II: 2021-22 edition (v1.1). Zenodo. https://doi.org/10.5281/zenodo.5532590

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