Editing Logistic regression (section)

=== General ===
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 ''{{Abbr|et al.|''et alia'', with others - usually other authors}}'' using logistic regression.<ref>{{cite journal| last1 = Boyd | first1 = C. R.| last2 = Tolson | first2 = M. A.| last3 = Copes | first3 = W. S.| title = Evaluating trauma care: The TRISS method. Trauma Score and the Injury Severity Score| journal = The Journal of Trauma| volume = 27 | issue = 4| pages = 370–378| year = 1987 | pmid = 3106646 | doi= 10.1097/00005373-198704000-00005| doi-access = free}}</ref>  Many other medical scales used to assess severity of a patient have been developed using logistic regression.<ref>{{cite journal |pmid= 11268952 |year= 2001|last1= Kologlu |first1= M.|title=Validation of MPI and PIA II in two different groups of patients with secondary peritonitis |journal=Hepato-Gastroenterology |volume= 48 |issue=37 |pages= 147–51 |last2=Elker|first2=D. |last3= Altun |first3= H. |last4= Sayek |first4= I.}}</ref><ref>{{cite journal |pmid= 11129812 |year= 2000 |last1= Biondo |first1= S. |title= Prognostic factors for mortality in left colonic peritonitis: A new scoring system |journal= Journal of the American College of Surgeons|volume= 191 |issue= 6 |pages= 635–42 |last2= Ramos|first2=E.|last3=Deiros |first3= M. |last4=Ragué|first4=J. M.|last5=De Oca |first5= J. |last6= Moreno |first6=P.|last7=Farran|first7=L.|last8= Jaurrieta |first8= E. |doi= 10.1016/S1072-7515(00)00758-4}}</ref><ref>{{cite journal|pmid=7587228 |year= 1995 |last1=Marshall |first1= J. C.|title=Multiple organ dysfunction score: A reliable descriptor of a complex clinical outcome|journal=Critical Care Medicine|volume= 23 |issue= 10|pages= 1638–52 |last2= Cook|first2=D. J.|last3=Christou|first3=N. V. |last4= Bernard |first4= G. R. |last5=Sprung|first5=C. L.|last6=Sibbald|first6=W. J.|doi= 10.1097/00003246-199510000-00007}}</ref><ref>{{cite journal|pmid=8254858|year=1993 |last1= Le Gall |first1= J. R.|title=A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study|journal=JAMA|volume=270|issue= 24 |pages= 2957–63 |last2= Lemeshow |first2=S.|last3=Saulnier|first3=F.|doi= 10.1001/jama.1993.03510240069035}}</ref> Logistic regression may be used to predict the risk of developing a given disease (e.g. [[Diabetes mellitus|diabetes]]; [[Coronary artery disease|coronary heart disease]]), based on observed characteristics of the patient (age, sex, [[body mass index]], results of various [[blood test]]s, etc.).<ref name="Freedman09">{{cite book |author=David A. Freedman |year=2009|title=Statistical Models: Theory and Practice |publisher=[[Cambridge University Press]]|page=128|author-link=David A. Freedman}}</ref><ref>{{cite journal | pmid = 6028270
| year = 1967
| last1 = Truett
| first1 = J
| title = A multivariate analysis of the risk of coronary heart disease in Framingham
| journal = Journal of Chronic Diseases
| volume = 20
| issue = 7
| pages = 511–24
| last2 = Cornfield| first2 = J| last3 = Kannel| first3 = W | doi= 10.1016/0021-9681(67)90082-3}}</ref>  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.<ref name="rms" /> The technique can also be used in [[engineering]], especially for predicting the probability of failure of a given process, system or product.<ref name="strano05">{{cite journal | author = M. Strano | author2 = B.M. Colosimo | year = 2006 | title = Logistic regression analysis for experimental determination of forming limit diagrams | journal = International Journal of Machine Tools and Manufacture | volume = 46 | issue = 6 | pages =  673–682 | doi = 10.1016/j.ijmachtools.2005.07.005 }}</ref><ref name="safety">{{cite journal | last1 = Palei | first1 = S. K. | last2 = Das | first2 = S. K. | doi = 10.1016/j.ssci.2008.01.002 | title = Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: An approach | journal = Safety Science | volume = 47 | pages = 88–96 | year = 2009 }}</ref> It is also used in [[marketing]] applications such as prediction of a customer's propensity to purchase a product or halt a subscription, etc.<ref>{{cite book|title=Data Mining Techniques For Marketing, Sales and Customer Support|last= Berry |first=Michael J.A|publisher=Wiley|year=1997|page=10}}</ref> In [[economics]], it can be used to predict the likelihood of a person ending up in the labor force, and a business application would be to predict the likelihood of a homeowner defaulting on a [[mortgage]]. [[Conditional random field]]s, an extension of logistic regression to sequential data, are used in [[natural language processing]]. Disaster planners and engineers  rely on these models to predict decisions taken by householders or building occupants in small-scale and large-scales evacuations, such as building fires, wildfires, hurricanes among others.<ref>{{Cite journal |last1=Mesa-Arango |first1=Rodrigo |last2=Hasan |first2=Samiul |last3=Ukkusuri |first3=Satish V. |last4=Murray-Tuite |first4=Pamela |date=February 2013 |title=Household-Level Model for Hurricane Evacuation Destination Type Choice Using Hurricane Ivan Data |url=https://ascelibrary.org/doi/10.1061/%28ASCE%29NH.1527-6996.0000083 |journal=Natural Hazards Review |language=en |volume=14 |issue=1 |pages=11–20 |doi=10.1061/(ASCE)NH.1527-6996.0000083 |bibcode=2013NHRev..14...11M |issn=1527-6988}}</ref><ref>{{Cite journal |last1=Wibbenmeyer |first1=Matthew J. |last2=Hand |first2=Michael S. |last3=Calkin |first3=David E. |last4=Venn |first4=Tyron J. |last5=Thompson |first5=Matthew P. |date=June 2013 |title=Risk Preferences in Strategic Wildfire Decision Making: A Choice Experiment with U.S. Wildfire Managers |url=https://onlinelibrary.wiley.com/doi/10.1111/j.1539-6924.2012.01894.x |journal=Risk Analysis |language=en |volume=33 |issue=6 |pages=1021–1037 |doi=10.1111/j.1539-6924.2012.01894.x |pmid=23078036 |bibcode=2013RiskA..33.1021W |s2cid=45282555 |issn=0272-4332}}</ref><ref>{{Cite journal |last1=Lovreglio |first1=Ruggiero |last2=Borri |first2=Dino |last3=dell’Olio |first3=Luigi |last4=Ibeas |first4=Angel |date=2014-02-01 |title=A discrete choice model based on random utilities for exit choice in emergency evacuations |url=https://www.sciencedirect.com/science/article/pii/S0925753513002294 |journal=Safety Science |volume=62 |pages=418–426 |doi=10.1016/j.ssci.2013.10.004 |issn=0925-7535}}</ref> These models help in the development of reliable [[Emergency management|disaster managing plans]] and safer design for the [[built environment]].