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International Journal of Injury Control and Safety Promotion, 2018
Simple and multiple linear regression models study the relationship between a single continuous dependent variable Y and one or multiple independent variables X, respectively (Bangdiwala, 2018a, 20...
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Simple and multiple linear regression models study the relationship between a single continuous dependent variable Y and one or multiple independent variables X, respectively (Bangdiwala, 2018a, 20...
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Logistic Regression for Correlated Binary Data
Applied Statistics, 1994Summary: The modelling of correlated binary outcomes, in such a way that the marginal response probabilities are still logistic, is considered. Different association measures for the dependence between correlated observations are discussed. For paired correlated data the full likelihood can be evaluated; for an arbitrary number of correlated ...
le Cessie, S., van Houwelingen, J. C.
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2013
This chapter discusses a methodology that is more or less analogous to linear regression discussed in the previous chapter, Binary Logistic Regression. In a binary logistic regression, a single dependent variable (categorical: two categories) is predicted from one or more independent variables (metric or non-metric). This chapter also explains what the
S. Sreejesh +2 more
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This chapter discusses a methodology that is more or less analogous to linear regression discussed in the previous chapter, Binary Logistic Regression. In a binary logistic regression, a single dependent variable (categorical: two categories) is predicted from one or more independent variables (metric or non-metric). This chapter also explains what the
S. Sreejesh +2 more
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Logistic Regression for Dependent Binary Observations
Biometrics, 1987The likelihood of a set of binary dependent outcomes, with or without explanatory variables, is expressed as a product of conditional probabilities each of which is assumed to be logistic. The models are called regressive logistic models. They provide a simple but relatively unknown parametrization of the multivariate distribution.
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2012
Unlike linear regression, which is used to classify or predict values on a continuous variable (e.g., estimated premorbid intelligence), logistic regression attempts to classify or predict a discrete, categorical variable from among continuous and/or discrete predictors, such as whether or not a patient will be successful in cognitive rehabilitation ...
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Unlike linear regression, which is used to classify or predict values on a continuous variable (e.g., estimated premorbid intelligence), logistic regression attempts to classify or predict a discrete, categorical variable from among continuous and/or discrete predictors, such as whether or not a patient will be successful in cognitive rehabilitation ...
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Binary Logistic Regression Using Survival Analysis
SSRN Electronic Journal, 2010Survival analysis problems have elsewhere been recast as problems in logistic regression, after the event times were grouped into intervals. Here we discuss the opposite connection: how binary logistic regression can be viewed fruitfully as a special case of accelerated failure time models in survival analysis.
Devlina Chatterjee, Anindya Chatterjee
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2016
Most people have heard the term “Odds”, and know that it has something to do with the likelihood of obtaining a “successful” outcome in some sort of game or trial. If P represents the probability of “success”, then 1 − P is the probability of “not success”. The odds are: $$ O=\frac{P}{1-P} $$
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Most people have heard the term “Odds”, and know that it has something to do with the likelihood of obtaining a “successful” outcome in some sort of game or trial. If P represents the probability of “success”, then 1 − P is the probability of “not success”. The odds are: $$ O=\frac{P}{1-P} $$
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Multicollinearity in Binary Logistic Regression Model
2021One of the key problems arises in binary logistic regression model is that explanatory variables being considered for the logistic regression model are highly correlated among themselves. Multicollinearity will cause unstable estimates and inaccurate variances that affects confidence intervals and hypothesis tests.
N. A. M. R. Senaviratna +1 more
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Binary Variables and Logistic Regression
1983In this chapter we consider generalized linear models in which the outcome variables are measured on a binary scale. For example, the responses may be alive or dead, or present or absent. ‘Success’ and ‘failure’ are used as generic terms for the two categories.
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Introduction to Binary Logistic Regression
2015Statistical inference with binary data presents many challenges, whether or not the observations are dependent or independent. Studies involving dependent observations tend to be longitudinal or clustered in nature, and therefore provide inefficient estimates if the correlation in the data is ignored.
Jeffrey R. Wilson, Kent A. Lorenz
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