A new explanatory index for evaluating the binary logistic regression based on the sensitivity of the estimated model

Ramos, Héctor M.; Ollero, Jorge; Suárez‐Llorens, Alfonso

doi:10.1016/j.spl.2016.08.022

Cited by 16 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The transport mode data in our study were categorized as the binary response variables, ‘low-carbon transport mode’ (walking/cycling, electric bike, bus and metro) and ‘non-low-carbon transport mode’ (private car and taxi), and a binary logistic regression model was adopted to examine the major determinants of socio-economic characteristics of travel behavior during shopping trips. The binary logic model for this study allows for the prediction of binary outcomes (a value of 1 with a probability of p for the respondents’ travel decisions of non-low-carbon transport mode and a value of 0 with probability 1- p for choosing low-carbon transport mode), using one or more continuous or categorical variables of socio-economic characteristics as predictors [ 63 , 64 ]. The binary logistic regression model can be written as Equation (1) [ 65 , 66 ].…”

Section: Methodsmentioning

confidence: 99%

Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China

Guo

2018

IJERPH

View full text Add to dashboard Cite

Choices regarding mode of travel have an evident effect on environment pollutants and public health. This paper makes a significant contribution by examining the differences between low-carbon and non-low-carbon travel mode choices during shopping trips, and how socio-economic characteristics impact individual travel behavior based on data gathered from a questionnaire conducted in Shenyang, China. The study found that, firstly, low-carbon travel modes were more common than non-low-carbon travel modes for shopping, and the average travel distance by non-low-carbon modes was a little longer than that of low-carbon modes. Secondly, suburban and wholesale specialized commercial centers attracted more residents travelling longer distances by non-low carbon modes, especially private car, compared to regional commercial centers in inner city areas. Thirdly, strong relationships between car ownership, gender, monthly income, and travel mode choices were identified in a binary logistic regression model. This study thus highlights the importance of sustainable transportation policies to advocate low-carbon travel modes and reduce carbon emissions.

show abstract

Section: Methodsmentioning

confidence: 99%

Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China

Guo

2018

IJERPH

View full text Add to dashboard Cite

show abstract

“…We used a binary logistic model to see which socio-economic characteristics were important for choosing between the two groups. The model predicts the probability (p) of selecting the sustainable group (1) or the non-sustainable group (0) with the probability (p − 1) based on a set of socio-economic features consisting of continuous and categorical variables (Ramos, Ollero, & Suárez-Llorens, 2017). Equation ( 1) shows the binary logistic model (Smallman-Raynor, Rafferty, & Cliff, 2017).…”

Section: Model Structurementioning

confidence: 99%

Cycling, Walking, And Public Transport Versus Private Cars: An Empirical Investigation Of Travel Mode Choices For Shopping Trips In Mumbai Metropolitan Region

Shkera,

Vaishali Patankar

2024

ToTS

View full text Add to dashboard Cite

This research delves into mode choice behavior for shopping-related excursions in the Mumbai Metropolitan Region (MMR), a rapidly expanding and socio-economically diverse urban area. The study seeks to identify key determinants influencing individuals' decisions between sustainable travel options, such as walking, cycling, and public transport, and private vehicles like cars. Utilizing empirical data from questionnaire surveys conducted on both weekdays and weekends, this study employed a binary logistic regression model to analyze the associations between mode preferences and pivotal factors, including car ownership, gender, income, age, walk score, and population density. Key findings underscored the prominence of sustainable transportation modes over private vehicles for shopping trips. Car ownership, gender, and income emerged as significant determinants shaping transportation decisions. The study emphasizes the need for policies prioritizing the enhancement of public transport infrastructure and services while promoting environmental sustainability. The research identifies areas for further investigation, suggesting a more nuanced approach to understand variations within demographic and built environment segments. Ultimately, the study provides insights to guide sustainable transportation policies, interventions, and urban planning in the MMR, advocating for a more sustainable and livable urban future.

show abstract

“…P-value is used to determine if any independent variable was statistically significant in the results of multiple logistic regression analysis on the training dataset, and independent variables with a P-value of 0.05 or higher are excluded. The parameters for statistically significant independent variables are as shown in (9). The regulation for removing an independent variable close to zero in order to make some coefficients zero is as shown in (7).…”

Section: Prediction Modelmentioning

confidence: 99%

“…However, since the value of the dependent variable is classified as pass or fail around the decision boundary, the value close to the decision boundary may be less accurate [6][7][8]. In binary logistic regression, since the actual value of the dependent variable is present and the predicted value can be calculated, the predicted value can be applied to a confusion matrix that can be compared to the target value [9,10]. It can be obtained sensitivity and precision from the confusion matrix using the actual and predicted values of the logistic regression, and apply it to algal blooms to create a summary of indicators such as sensitivity and precision including accuracy [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Prediction model of algal blooms using logistic regression and confusion matrix

Yun

2021

IJECE

View full text Add to dashboard Cite

Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.

show abstract

A new explanatory index for evaluating the binary logistic regression based on the sensitivity of the estimated model

Cited by 16 publications

References 10 publications

Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China

Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China

Cycling, Walking, And Public Transport Versus Private Cars: An Empirical Investigation Of Travel Mode Choices For Shopping Trips In Mumbai Metropolitan Region

Prediction model of algal blooms using logistic regression and confusion matrix

Contact Info

Product

Resources

About