Question

I want to reformulate the solution, using other words

Logistic regression

It 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 et al. using logistic regression. Many other medical scales used to assess severity of a patient have been developed using logistic regression.

• 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.
• Another example might be to predict whether an Indian voter will vote BJP or Trinomial Congress or Left Front or Congress, 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.
• 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.).

Answer 1

Long regression method is used in multiple number of fields such as engineering, medicine and science. From the examples given below, we can understood that this method is used to predict something with other. The following are the examples,

1. In medicine, the Trauma and Injury Severity Score was designed (Boyd et.al) long regression to predict the mortality or death rate in injured patients. Moreover, to find out the severity of the patient condition, again thanks to the long regression which helped to develop. In addition to this, it also predict the chance of occurring a disease to patient's age,sex,body mass index distribution and blood values.

2. In marketing an applications, long regression can be used to predict the customer's tendency to purchase or stop a product subscription.

3. In economics, the likelihood of a individual interest in labor force is used to predict with the likelihood of the homeowner on a mortgage.

4. At the time of election, long regression can be used to predict who vote to whom based on age,income,sex,race ,state of living and past votes data.