Medical economics

Study investigates using telemedicine for flu diagnosis

Imagine you're feeling achy. You have a cough, and you might have a fever. It's flu season, so you want to have a doctor check you out. Almost a quarter of Americans now opt for a telehealth visit, which public health experts ...


New tool reveals the impact of 'jumping genes' on diseases

RIKEN geneticists have developed a tool that can quickly and accurately analyze variants in mobile genetic elements, commonly known as "jumping genes." This promises to shed light on the role such variants play in disease.

Diseases, Conditions, Syndromes

Analyzing cough sounds to identify the severity of COVID-19 patients

While most individuals impacted by COVID-19 experience milder symptoms and recover within a few weeks, the global pandemic caused by the SARS-CoV-2 virus continues to pose a significant health challenge. Some of those affected ...

Oncology & Cancer

New model predicts 10-year breast cancer risk

A team of researchers at the University of Oxford, led by the Nuffield Department of Primary Care Health Sciences, have developed a new model that reliably predicts a woman's likelihood of developing and then dying of breast ...


New disease surveillance can inform public health responses

A team of researchers, including Te Whare Wānanga o Waitaha | University of Canterbury (UC) Dr. Leighton Watson and Professor Michael Plank, combined wastewater data with reported case numbers to create a statistical model ...

page 1 from 19

Statistical model

A statistical model is a set of mathematical equations which describe the behavior of an object of study in terms of random variables and their associated probability distributions. If the model has only one equation it is called a single-equation model, whereas if it has more than one equation, it is known as a multiple-equation model.

In mathematical terms, a statistical model is frequently thought of as a pair (Y,P) where Y is the set of possible observations and P the set of possible probability distributions on Y. It is assumed that there is a distinct element of P which generates the observed data. Statistical inference enables us to make statements about which element(s) of this set are likely to be the true one.

Three notions are sufficient to describe all statistical models.

One of the most basic models is the simple linear regression model which assumes a relationship between two random variables Y and X. For instance, one may want to linearly explain child mortality in a given country by its GDP. This is a statistical model because the relationship need not to be perfect and the model includes a disturbance term which accounts for other effects on child mortality other than GDP.

As a second example, Bayes theorem in its raw form may be intractable, but assuming a general model H allows it to become

which may be easier. Models can also be compared using measures such as Bayes factors or mean square error.

This text uses material from Wikipedia, licensed under CC BY-SA