Diseases, Conditions, Syndromes

Understanding the spread of infectious diseases

Scientists worldwide have been working feverishly on research into infectious diseases in the wake of the global outbreak of the COVID-19 disease, caused by the new coronavirus SARS-CoV-2. This concerns not only virologists, ...

Diseases, Conditions, Syndromes

California's feared surge of virus cases hasn't happened

Near the end of September, with coronavirus cases falling and more schools and businesses reopening, Gov. Gavin Newsom's administration urged restraint, citing a statistical model that predicted a startling 89% increase in ...

Medical research

Statistical model improves analysis of skin conductance

Electrodermal activity—the sweat-induced fluctuations of skin conductance made famous in TV dramatizations of lie-detector tests—can be a truly strong indicator of subconscious, or "sympathetic," nervous system activity ...

Obstetrics & gynaecology

Mathematical modelling to prevent fistulas

It is better to invest in measures that make it easier for women to visit a doctor during pregnancy than measures to repair birth injuries. This is the conclusion from two mathematicians at LiU, using Uganda as an example.

Diseases, Conditions, Syndromes

Online tool informs recovery prospects for sepsis survivors

The tool, available online, is the first of its kind and was developed and validated using anonymised data from around 120,000 sepsis patients from the ICNARC national database for critical care units across England.

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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