one paged quick learning so you can hit the ground running while on the job
More Statistical Understanding
Following on from the understanding basic statistics page, this page provides some information on calculating sample sizes, different types of regression models and other multi-variate analysis. This may be of use if you're doing surveillance, epidemiology or other modelling for health intelligence.
Multivariate analysis encompasses a variety of statistical methods used to analyse measurements on two or more variables. Regression analysis is a major subset of multivariate analysis that includes methods for predicting values of one or more response variables from one or more predictor variables.
A model is a description of a relationship connecting the variables of interest. It becomes a statistical model when it is fitted to sample data with the aim of generalising beyond the sample to the underlying population from which the sample was drawn.
You will come across the terms 'Bayesian' and 'Frequentists'. Bayesian methods make statements about the relative evidence for parameter values given a dataset. Frequentists compare relative chance of datasets given a parameter value. Bayesian statistics starts from what has been observed and assesses possible future outcomes. Frequentist (or classical) statistics starts with an abstract experiment of what would be observed if one assumes something, and only then compares the outcomes of the abstract experiment with what was actually observed. The key difference for me is that Bayesians say we have prior information about the outcome and use this information in their modelling. To illustrate, if you lose your car keys, a frequentist will use a model to determine the likelihood of where you lost it and infer which area you should search. A Bayesian will note the places you've been since last seeing your car keys and use this information to adapt the model and limit the areas where you should search.