Top Research Papers on Logistic Regression

1 Many statistical tests require the dependent (response) variable to be continuous so a different set of tests are needed when the dependent variable is categorical. One of the most commonly used tests for categorical variables is the Chi-squared test which looks at whether or not there is a relationship between two categorical variables but this doesn’t make an allowance for the potential influence of other explanatory variables on that relationship. For continuous outcome variables, Multiple regression can be used for

It was commented that reoperation was nearly twice as likely when the tumour had a carcinoma in situ component recorded, and the association between breast reoperation and patients’ characteristics was examined.

Like contingency table analyses and 2 tests, logistic regression allows the analysis of dichotomous or binary outcomes with 2 mutually exclusive levels.1 However, logistic regression permits the use of continuous or categorical predictors and provides the ability to adjust for multiple predictors. This makes logistic regression especially useful for analysis of observational data when adjustment is needed to reduce the potential bias resulting from differences in the groups being compared.2 Use of standard linear regression for a 2-level outcome can produce very unsatisfactory results. Predict...

Predicting dichotomous outcomes is central to epidemiologic science and clinical care and is used to describe and test the relationship between a dichotomyous outcome and one or more potentially predictive variables.

The main advantage is that the output of the prediction equation is a probability on a proper 0-1 scale, which means that the authors have some direct and natural measure of the uncertainty in the assignment.

This technical note presents the reason for using a binomial logic regression in marketing applications and a consumer-utility-based behavioral rationale is presented for the applicability of the binomial logistic regression for modeling dummy variables.

Linear regression modeling is well suited to predicting continuous data where the outcome y is a real number (i.e., y ∈ ℝ). Logistic regression is a modeling technique for binary outcomes (i.e., yes/no, true/false, 1/0). Such outcomes are needed in many domains: public health officials might want to know the likelihood that a person will contract COVID-19 if she is a doctor in Ontario;a hospital would like to know if a discharged patient is more likely to be readmitted or not;a company would like to know if a customer visiting its website is more likely to order;a bank would like to know if a ...

Logistic regression has probably been underutilized in clinical investigations of personality because of its relatively recent development (dictated by the need for computer programs to obtain maximum likelihood estimates), and the fact that use has been largely confined to the fields of biostatistics, epidemiology, and economics Its use should be given serious consideration when the outcome of interest is dichotomous (or polychotomous) in nature and the predictors of interest may be categorical or continuous. The logit transformation is quite tractable mathematically, and it embodies the noti...

I am very glad I was asked to read the new edition of the book, and I found it to be a useful resource that belongs on the required book lists for personnel preparation programs.

Logistic regression is a very flexible tool to study the relationship between several explanatory variables and a binary response. To fit and examine such a logistic regression model, some tools used with generalized linear models, as introduced by McCullagh and Nelder in 6, are useful. Once the model is fitted and the goodness-of-fit has been examined, the relationship between covariates and the response can be better understood by interpreting the coefficients in the logistic model in terms of odds ratios. The model can also be used to make predictions. As such, the logistic model provides a...

One feature of the usual polychotomous logistic regression model for categorical outcomes is that a covariate must be included in all the regression equations. If a covariate is not important in all of them, the procedure will estimate unnecessary parameters. More flexible approaches allow different subsets of covariates in different regressions. One alternative uses individualized regressions which express the polychotomous model as a series of dichotomous models. Another uses a model in which a reduced set of parameters is simultaneously estimated for all the regressions. Large-sample effici...

The 4th printing enhances Stata code to use version 11 rather than version 9-10 code. The book was completed before Stata version 11 was published. For example, when constructing synthetic data, the book now uses the new Stata pseudo-random number generators rather than the ones I created back in 1995 – the suite of rnd* commands -or Roberto Gutierrez’s unpublished genbinomial command. No more corrections to the text are planned for future printings. A second edition is planned to be published in 2013 and will include nested logistic regression, and chapters on latent class models and on Bayes...

The focus of this document is on situations in which the outcome variable is dichotomous, although extension of the techniques of LRA to outcomes with three or more categories is possible.

This book discusses the normal model foundation of the Binomial Model, the nature and Scope of Overdispersion, and how to model and estimate these phenomena.

The logistic regression in generalized additive models is better than linear regression of arcsine square root transformed data in following ways: reasonable predictions about diapause ranging from 0 to 1 can be made without transforming the proportional data; non-linear effects of temperature and day-length on diAPause can be determined; and the goodness-of-fit can be substantially improved.

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Based on the experience of teaching logistic regression to non-mathematicians, a number of areas of possible confusion are identified that may arise particularly when the method is contrasted with multiple linear regression. The fact that the model is multiplicative in odds ratios means that the concept of interaction needs to be clearly defined. Confidence intervals for the estimates of the odds ratios are asymmetric about the estimate, in contrast to confidence intervals in multiple regression which are symmetric. The fact that including a covariate will often increase the standard error of ...

This work uses a different approach called the logistic regression that does not require computing the p-value and still be able to localize the regions of brain network differences and performs the classification at each edge level.

The fact that the maximum likelihood estimate in a logistic regression model may not exist is a well-known phenomenon and a number of recent papers have explored its underlying geometrical basis. [9], [12] and [7] point out that existence, and non-existence, of the estimate can be fully characterised by considering the closure of the model as an exponential family. In this formulation it becomes clear that the maximum is always well-defined, but can lie on the boundary rather than in the relative interior. Furthermore, the boundary can be considered as a polytope characterised by a finite numb...

A primer to quickly impart a working knowledge of logistic regression in SAS®, using examples to demonstrate the LOGISTIC procedure’s basic syntax, model construction and selection options, and output interpretation.