logit-model
There are 0 repository under logit-model topic.
- logitr
jhelvy / logitr
Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R with "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations in R
Language:R 39 Valmir-unicap / PET-Pickcells
Projeto de Extensão Tecnológica (PET) - Equipe Gama
Language:Jupyter Notebook 9alan-turing-institute / discrete-choice
Unifying interface for discrete choice modelling
Language:Python 3BendeguzBiksi / Master-Studies-Econometrics-II
Master Degree Coursework: Econometrics II
Language:Stata 3drsquareminha / Aggregate-Latent-Class-Logit
R code for estimation of latent class logit model with aggregate data
Language:R 2salilrsharma / GAMSlogit
In this repository, a GAMS code is provided to estimate parameters of the logit model. We provide a code and sample data to solve for binary route choice model for truck drivers.
Language:GAMS 2vaitybharati / Assignment-06-Logistic-Regression
Assignment-06-Logistic-Regression. Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no") Attribute information For bank dataset Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no") 8. Missing Attribute Values: None
Language:Jupyter Notebook 2magerton / OrderedResponse.jl
Ordered logit and probit models
Language:Julia 1moon1863 / Mode-choice-for-commodity-flow
Prediction of freight transportation mode
Language:Jupyter Notebook 1shivaniramdas / Predictive-Analytics-SAS
Estimated probability models that help target consumers to build brand loyalty
Language:SAS 1chomiczdawid / parametric-statistical-models
This repository consist of analytical exercises about parametric statistical models done in Rmarkdown.
Language:HTML 0gkukish / CEO-Dismissal-Causal-Inference
Researching the relationship between stock market performance and involuntary CEO dismissal.
Language:Jupyter Notebook 0Researching the relationship between stock market performance and involuntary CEO dismissal.
Language:Jupyter Notebook 0JuitingHu / The-attrition-of-Shanghai-Ctrip-call-centre
Using R to analyse the relationship between variables and attrition in Shanghai Ctrip call centre's WFH data.
Language:R 0mx-eyebrows / Categorical-Analysis
Final project for PHST 684 at the University of Louisville (Categorical Data Analysis)
Language:R 0nhb-uyen / Probit-logit-poisson
Hypothesis Testing Assignment - Ordered choice model. Using probit, logit and Poisson models to solve problems based on economic theory.
0PatilSukanya / Assignment-06.-Logistic-Regression
Used libraries and functions as follows:
Language:Jupyter Notebook 0piazzai / chess-variants
Analysis of Lichess variant games
Language:R 0Yoonyoung-Cho / 2019_Telecom_Customer_Secession_Prediction
2019.12.18 개인프로젝트. 통신사 고객들의 이탈 예측
Language:R 0- Language:Makefile
