The coefficients are generally not computed by a closed-form expression, unlike linear least squares; see . The logistic regression as a general statistical model was originally developed and popularized primarily by Joseph Berkson, beginning in , where he coined "logit"; see . Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score (TRISS), which is widely used to predict mortality in injured patients, was originally developed by Boyd "" using logistic regression. Many other medical scales used to assess severity of a patient have been developed using logistic regression. Logistic regression may be used to predict the risk of developing a given disease (e.g. diabetes; coronary heart disease), based on observed characteristics of the patient (age, sex, body mass index, results of various blood tests, etc.). Another example might be to predict whether a Nepalese voter will vote Nepali Congress or Communist Party of Nepal or Any Other Party, based on age, income, sex, race, state of residence, votes in previous elections, etc. The technique can also be used in engineering, especially for predicting the probability of failure of a given process, system or product. It is also used in marketing applications such as prediction of a customer's propensity to purchase a product or halt a subscription, etc. In economics it can be used to predict the likelihood of a person's choosing to be in the labor force, and a business application would be to predict the likelihood of a homeowner defaulting on a mortgage. Conditional random fields, an extension of logistic regression to sequential data, are used in natural language processing. Let us try to understand logistic regression by considering a logistic model with given parameters, then seeing how the coefficients can be estimated from data. Consider a model with two predictors, formula_1 and formula_2, and one binary (Bernoulli) response variable formula_3, which we denote formula_4. We assume a linear relationship between the predictor variables and the log-odds of the event that formula_5. This linear relationship can be written in the following mathematical form (where "ℓ" is the log-odds, formula_6 is the base of the logarithm, and formula_7 are parameters of the model):  We can recover the odds by exponentiating the log-odds:  By simple algebraic manipulation, the probability that formula_5 is  The above formula shows that once formula_7 are fixed, we can easily compute either the log-odds that formula_5 for a given observation, or the probability that formula_14 for a given observation.