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# What is Logistic Regression

A logistic regression is a type of statistical model typically using a logistic function to see what predicts a categorical dependent variable. In this case the categories are a) the certification has a military designation or b) it does not have the designation.
Published in Chapter:
Military-to-Civilian Transition Through Credentials: Certification Promotion by Military COOL Program
Mary Tschirhart (The George Washington University, USA) and Huang Chen (The George Washington University, USA)
DOI: 10.4018/978-1-7998-3820-3.ch009
Abstract
This chapter reviews the United States COOL programs' promotion of certification during military employment to support transition to civilian employment and the CareerOneStop platform which profiles certifications. Some certifications on CareerOneStop have a designation from COOL indicating relevance to military workers. The chapter presents analyses showing that certain types of certifications are more likely to have a military designation than others. In brief, the designation is more likely for accredited and industry-recognized certifications and those tied to occupations with lower annual median wages and predictions of decrease and increase in employment versus a more stable trajectory. Some occupations also significantly differ in the likelihood of a military designation for certifications tied to it. The authors close with a discussion of recommendations including additional questions for consideration.
More Results
A regression model where the dependent variable takes on a limited number of discrete values, often two values representing yes and no.
A regression model that is used when the dependent variable is qualitative and a probability is assigned to an observation for the likelihood that the target variable has a value of 1.
Logistic regression analysis is mainly used in epidemiology. The most common case is to explore the risk factors of a certain disease and predict the probability of the occurrence of a certain disease according to the risk factors.
Logistic regression is used when the response variable is categorical in nature.
It is an algorithm used in the software to understand the relation between dependent and independent variables by estimating probabilities using logistic regression equation. This algorithm helps you to predict the likeliness on an event happening.
the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary).
Logistic regression is a type of regression analysis used for predicting the outcome of categorical dependent variable based on one or more predictor variables.
Regression for dichotomous data
This is a kind of regression analysis often used when the outcome variable is dichotomous and scored 0, 1. Logistic regression is also known as logit regression and when the dependent variable has more than two categories it is called multinomial. Logistic regression is used when predicting whether an event will happen or not.
is used to quantify the predictability, and then the classifier is employed. For example, it can anticipate or provide a true or false result. Researchers in determined to engage this classifier.
Logistic regression is a method of statistical modeling appropriate for categorical outcome variables. It describes the relationship between a categorical response variable and a set of explanatory variables.
Technique for making predictions when a dependent variable is a categorical dichotomy, and the independent variable(s) are continuous and/or categorical.
Statistical regression model for Bernoulli-distributed dependent variables. It is a generalized linear model that uses the logit as its link function. Logistic regression applies maximum likelihood estimation after transforming the dependent into a logit variable (the natural log of the odds of the dependent occurring or not).
LR is a classification problem-solving supervised ML technique. With the exception of how they are applied, LR and linear regression are very similar. While LR is used to solve classification problems, linear regression is used to solve regression problems ( Ferawati et al., 2022 ).
Logistic regression is a statistical method for determining the relationship between independent predictor variables (such as financial ratios) and a dichotomously coded dependent variable (such as default or non-default).
A regression model built using exponential functions for dichotomous variables; usually non-linear in nature.
It is classification algorithm and used to predict customer will churn or not churn.