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The phrase "a logistics regression model" is not correct in English; it should be "a logistic regression model." You can use it when discussing statistical models used for binary classification tasks in data analysis or machine learning.
Example: "In our research, we applied a logistic regression model to predict the likelihood of customer churn based on various factors."
Alternatives: "a logistic model" or "a regression model for logistics."
Exact(1)
All clinical features with ρ < 0.10 were entered into a logistics regression model.
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The primary outcomes were analyzed by univariate and multivariate Cox regression model and the logistics regression model.
These statistically significant variables were found in the univariate analysis (P, 0.05), and the Logistics Regression Model.
Odds ratios (and 95% confidence intervals) for back pain were obtained from logistics regression models and log-linear backward elimination analysis was performed.
Factor response was defined as a 50% improvement (reduction) in factor score from baseline to week 3. Receiver operating characteristic curves were constructed through the use of logistics regression models for each of the three treatment groups: aripiprazole treatment, comparative treatment (haloperidol and lithium combined), and placebo.
In these logistics regression models, demographics, socioeconomic status, and health practice are controlled for.
To address the research objective, a literature review was completed; an expert panel was formed and consulted; a conceptual model was developed; a telephone interview survey was designed; an exploratory factor analysis was performed; and finally, a logistics regression analysis was performed.
Because of the discrete binary nature of the dependent variable, a logistics regression was used.
*p < 0.05; **p < 0.01; ***p < 0.001; ORU: univariate odds ratio obtained from logistic regression models; ORm: odds ratios obtained from stepwise multivariate logistics regression analysis, using univariately significant variables as candidate variables; NS: not statistically significant in multivariate analysis.
Logistics multiple regression models were established for winter and summer conditions, and influences of individual factors on thermal satisfaction of the elderly in free running environments were analyzed.
Consider a linear regression model.
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