Statistical Analysis Explained
Dr. Jo Roislien Science Communicator; Mathematician; Biostatistician; Professor of Medical Statistics at University of Stavanger, Norway
Learn the A-Z of scientific data and statistical analysis, from univariate and bivariate analysis to regression analysis, with pragmatic advice, easy-to-apply tips, and detailed explanations by a renowned biostatistician.
What you learn
Two types of scientific data and how to work with it
Understanding the relationship between variables
What is continuous and categorical data
How is each type of data represented
Data in univariate analysis and bivariate analysis
Choosing the right type of test for your data
Basic statistical methods for categorical and continuous data
What is regression analysis
Different types of regression models
Using the most suitable regression model for your variables
As a researcher, you will often need to collect, sort and analyze large amounts of complex data for your study. However, this can be a daunting task for most and comes with the risk of errors and inconsistencies, which can ring the death knell for your study. Often, researchers are confused between the different methods of data analysis, when to use each type of test, and how to ensure this is done correctly. This comprehensive two-part program, in the form of a webinar by renowned biostatistician Dr. Jo Røislien, is designed to simplify statistical analysis for researchers.
In the first session, you will learn that the answer to the question ‘how to handle data properly’ depends on the ‘type of data’ you are dealing with, which is the key to accurate and appropriate data analysis. You will be introduced to the main types of data, continuous or categorical, and gain an overview of the ideal statistical analysis method – univariate or bivariate – for each type of data. The program also offers pragmatic advice on how to style your research, including how to describe, compare and identify the links between types of variables.
The second session delves into the types of regression analysis – univariate and multivariable – and explains how you can incorporate a powerful framework to study your data most effectively. With helpful guidelines, practical tips, and easy-to-understand illustrations and clear examples, this program is perfect for researchers across subject areas who wish to understand or improve on how they describe, analyze, and evaluate data.