The phrase "a logit regression model" is correct and usable in written English.
You can use it when discussing statistical methods, particularly in the context of modeling binary outcomes.
Example: "In our analysis, we employed a logit regression model to predict the likelihood of customer churn based on various factors."
Alternatives: "a logistic regression model" or "a logit model".
The data for the analysis are drawn from the survey Encuesta de Mayores 2010 (IMSERSO) by means of different specifications of Logit techniques, namely a Logit Regression Model and a Multinomial Logit Regression Model.
For the technically minded Citi kindly explains it is using a logit regression model, expressed as: p i,t) = 1/1 + e(to the power of)- βx i,t) where: x i,t) is a vector of fundamental characteristics of the firm; β is a vector of factor sensitivities; p i,t) is the probability that a firm i will be conduct a rights issue in period t.
This paper aims to identify factors that may have potential to either promote or hinder fishers' participation, and investigate the association between factors and fishers' intention to participate using a logit regression model.
The best predictions were those of (i) a classification tree based on farmers' indicators of erosion (Pearson's Phi correlation coefficient ρ = 0.72, n = 334, α < 0.01); (ii) a semi-empirical model that accounted for overland flow reinfiltration (ρ = 0.43); and (iii) a logit regression model based on slope and ground cover (ρ = 0.34).
To investigate the effect of descriptive variables, such as economic and social factors, on the WTP, we used a logit regression model.
To examine the influence of the proposed explanatory variables on perceived entrepreneurial ability a logit regression model is used (Greene, 2003).
For a sample of 1397 trips, we fitted a multinomial logit regression model.
Additionally, all analyses, i.e., the full sample and the robustness checks, were re-estimated using a multinomial logit regression model (MNL), which estimates the probability of a change in the number of physician visits after April 2009.
Here we describe a method to monitor changes in habitat types by producing sequential maps based on point information followed by mapping using a multinomial logit regression model with abiotic variables of which maps are available as predictors.
We also particularly wanted to compare the strength of this relationship between good and poor readers directly by testing, through use of a multilevel logit regression model, the extent to which these variables predicted the likelihood of the students in the sample being good or poor readers.
For multivariate AORs, a multilevel logit regression model was used with 'bed sharing' random across studies.
