Public Health has been defined as “the art and science of preventing disease, prolonging life and promoting health through the organized efforts of society”. Knowing what causes disease and what makes it worse are clearly vital parts of this. This requires the development of statistical models that describe how patient and environmental factors affect our chances of getting ill. This course will show you how to create such models from scratch, beginning with introducing you to the concept of correlation and linear regression before walking you through importing and examining your data, and then showing you how to fit models. Using the example of respiratory disease, these models will describe how patient and other factors affect outcomes such as lung function.
Linear regression is one of a family of regression models, and the other courses in this series will cover two further members. Regression models have many things in common with each other, though the mathematical details differ.
This course will show you how to prepare the data, assess how well the model fits the data, and test its underlying assumptions – vital tasks with any type of regression.
You will use the free and versatile software package R, used by statisticians and data scientists in academia, governments and industry worldwide.