Logistic Regression, Logit Models, and Logistic Discrimination

2006 ◽  
pp. 116-177 ◽  
Keyword(s):  
Logistic Regression ◽  
Logit Models ◽  
Logistic Discrimination

CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS

2011 ◽  
Vol 03 (03) ◽  
pp. 309-324 ◽  
Author(s):  
STAN LIPOVETSKY
Keyword(s):  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Link Function ◽  
Logit Models ◽  
Categorical Variable ◽  
Multinomial Models ◽  
Multinomial Logit Models ◽  
Dependent Variables ◽  
Binary Logit ◽  
Logit Modeling

For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations. The paper shows that by a special rearrangement of data the more complicated conditional and multinomial models can be reduced to binary logistic regression. It suggests the usage of any software widely available for logit modeling to facilitate constructing for complex conditional and multinomial regressions. In addition, for binary logit, it is possible to obtain meaningful coefficients of regression by transforming data to the linear link function, which opens a possibility to obtain meaningful parameters of the complicated models with categorical dependent variables.

scholarly journals PREDICTING FINANCIAL DISTRESS IN THE MALAYSIAN MARKET: HAZARD MODEL VERSUS THE LOGIT MODEL

2021 ◽  
Vol 3 (7) ◽  
pp. 01-19
Author(s):  
Ahmad Harith Ashrofie Hanafi ◽  
Rohani Md-Rus ◽  
Kamarun Nisham Taufil Mohd
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Financial Distress ◽  
Logit Model ◽  
Hazard Model ◽  
Financial Ratios ◽  
Logit Models ◽  
Listed Firms ◽  
Distressed Firms ◽  
Model Versus

The increasing numbers of financially distressed firms in the Malaysian market demonstrate the importance of predicting financial distress among firms in Malaysia. Using firm financial ratios, this study focuses on predicting financial distress using the hazard model and logistic regression (logit model) based on the Malaysian market. This study used listed firms on the Malaysian stock market from 2000 to 2018 to create two sets of data comprising the main sample and holdout sample in order to compare the predictability between hazard and logit models. The results clearly show that the hazard model is better compared to the logit model in predicting financial distress for the Malaysian market since more variables were found to be significant in addition to the model being more consistent in terms of accuracy.

How Useful is the Power Law of Practice for Recognizing Practice in Concentration Tests?

2007 ◽  
Vol 23 (3) ◽  
pp. 157-165 ◽  
Author(s):  
Carmen Hagemeister
Keyword(s):  
Logistic Regression ◽  
Reaction Time ◽  
Power Law ◽  
Error Rate ◽  
Reaction Times ◽  
Practice Effect ◽  
Practice Effects ◽  
Rate Decrease ◽  
Small Practice

Abstract. When concentration tests are completed repeatedly, reaction time and error rate decrease considerably, but the underlying ability does not improve. In order to overcome this validity problem this study aimed to test if the practice effect between tests and within tests can be useful in determining whether persons have already completed this test. The power law of practice postulates that practice effects are greater in unpracticed than in practiced persons. Two experiments were carried out in which the participants completed the same tests at the beginning and at the end of two test sessions set about 3 days apart. In both experiments, the logistic regression could indeed classify persons according to previous practice through the practice effect between the tests at the beginning and at the end of the session, and, less well but still significantly, through the practice effect within the first test of the session. Further analyses showed that the practice effects correlated more highly with the initial performance than was to be expected for mathematical reasons; typically persons with long reaction times have larger practice effects. Thus, small practice effects alone do not allow one to conclude that a person has worked on the test before.

Perceived Effectiveness of Random Testing for Alcohol and Drugs in the Australian Aviation Community

2012 ◽  
Vol 2 (2) ◽  
pp. 72-81
Author(s):  
Christina M. Rudin-Brown ◽  
Eve Mitsopoulos-Rubens ◽  
Michael G. Lenné
Keyword(s):  
Logistic Regression ◽  
Online Survey ◽  
Random Testing ◽  
Regression Analyses ◽  
Perceived Effectiveness ◽  
Student Pilots ◽  
Alcohol And Drugs ◽  
Positive Attitudes ◽  
One Year ◽  
Alcohol And Other Drugs

Random testing for alcohol and other drugs (AODs) in individuals who perform safety-sensitive activities as part of their aviation role was introduced in Australia in April 2009. One year later, an online survey (N = 2,226) was conducted to investigate attitudes, behaviors, and knowledge regarding random testing and to gauge perceptions regarding its effectiveness. Private, recreational, and student pilots were less likely than industry personnel to report being aware of the requirement (86.5% versus 97.1%), to have undergone testing (76.5% versus 96.1%), and to know of others who had undergone testing (39.9% versus 84.3%), and they had more positive attitudes toward random testing than industry personnel. However, logistic regression analyses indicated that random testing is more effective at deterring AOD use among industry personnel.

Do logistic regression and signal detection identify different subgroups at risk? Implications for the design of tailored interventions.

2001 ◽  
Vol 6 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Michaela Kiernan ◽  
Helena C. Kraemer ◽  
Marilyn A. Winkleby ◽  
Abby C. King ◽  
C. Barr Taylor
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Signal Detection ◽  
Tailored Interventions
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