... CONTENTS -- 1 Introduction to the Logistic Regression Model -- 1.1 Introduction, 1 -- 1.2 Fitting the ... ... Regression -- 2.1 Introduction, 31 -- 2.2 The Multiple Logistic Regression Model, 31 -- 2.3 Fitting ... ... Regression, 273 -- 8.1.4 Assessment of Fit and Diagnostics for the Multinomial Logistic Regression Model ... ... Regression Models, 305 -- 8.3 Logistic Regression Models for the Analysis of Correlated Data, 308 -- ... ... 8.4 Exact Methods for Logistic Regression Models, 330 -- 8.5 Sample Size Issues When Fitting Logistic ...
Wiley series in probability and statistics
2nd ed. xii, 375 s.
Springer series in statistics
1st ed. 568 s.
The importance of environmental sustainability is becoming more and more obvious, so the rationale behind long-term usage of solely non-renewable energy sources appeared questionable. This study aims to identify, using Kaplan-Meier survival analysis and logistic regressions, the main determinants that affect the duration of Russian non-renewable energy exports to different regions of the world. Data were retrieved from the databanks of the World Development Indicators (WDI), World Integrated Trade Solution (WITS), and the French Centre for Prospective studies and International Information (CEPII). The obtained results point to the fact that approximately 52% of energy products survive beyond their first year of trading, nearly 38% survive beyond the second year, and almost 18% survive to the twelfth year. The survival of Russian non-renewable energy exports differs depending on the region, and the affecting factors are of different importance. The duration of Russian non-renewable energy exports is significantly linked to Russia's GDP, Total export, and Initial export values. A decline in Russia's GDP by 1% is associated with an increasing probability of a spell ending by 2.9% on average, in turn growing Total export and Initial export values positively linked with the duration of non-renewable energy exports from Russia. These findings may have practical relevance for strategic actions aimed at approaching both energy security and environmental sustainability.
- MeSH
- Economic Development * MeSH
- Logistic Models MeSH
- Carbon Dioxide * MeSH
- Prospective Studies MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Russia MeSH
... Linear Regression and the Logistic Regression Model 1 -- 1.1 Regression Assumptions 4 -- 1.2 Nonlinear ... ... Summary Statistics for Evaluating the Logistic -- Regression Model 17 -- 2.1 R2, F, and Sums of Squared ... ... 36 -- 2.5 Conclusion: Summary Measures for Evaluating the -- Logistic Regression Model 41 -- 3. ... ... Interpreting the Logistic Regression Coefficients 41 -- 3.1 Statistical Significance in Logistic Regression ... ... Polytomous Logistic Regression and Alternatives to -- Logistic Regression 91 -- 5.1 Polytomous Nominal ...
Sage university papers series Quantitative applications in t he social sciences ; No. 07-106
2nd ed. viii, 111 s. : il.
At the beginning of 2020 there was a spinning point in the travel behavior of people around the world because of the pandemic and its consequences. This paper analyzes the specific behavior of travelers commuting to work or school during the COVID-19 pandemic based on a sample of 2000 respondents from two countries. We obtained data from an online survey, applying multinomial regression analysis. The results demonstrate the multinomial model with an accuracy of almost 70% that estimates the most used modes of transport (walking, public transport, car) based on independent variables. The respondents preferred the car as the most frequently used means of transport. However, commuters without car prefer public transport to walking. This prediction model could be a tool for planning and creating transport policy, especially in exceptional cases such as the limitation of public transport activities. Therefore, predicting travel behavior is essential for policymaking based on people's travel needs.
- MeSH
- COVID-19 * MeSH
- Bicycling MeSH
- Transportation MeSH
- Humans MeSH
- Logistic Models MeSH
- Pandemics * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: Vaccine efficacy (VE) assessed in a randomized controlled clinical trial can be affected by demographic, clinical, and other subject-specific characteristics evaluated as baseline covariates. Understanding the effect of covariates on efficacy is key to decisions by vaccine developers and public health authorities. METHODS: This work evaluates the impact of including correlate of protection (CoP) data in logistic regression on its performance in identifying statistically and clinically significant covariates in settings typical for a vaccine phase 3 trial. The proposed approach uses CoP data and covariate data as predictors of clinical outcome (diseased versus non-diseased) and is compared to logistic regression (without CoP data) to relate vaccination status and covariate data to clinical outcome. RESULTS: Clinical trial simulations, in which the true relationship between CoP data and clinical outcome probability is a sigmoid function, show that use of CoP data increases the positive predictive value for detection of a covariate effect. If the true relationship is characterized by a decreasing convex function, use of CoP data does not substantially change positive or negative predictive value. In either scenario, vaccine efficacy is estimated more precisely (i.e., confidence intervals are narrower) in covariate-defined subgroups if CoP data are used, implying that using CoP data increases the ability to determine clinical significance of baseline covariate effects on efficacy. CONCLUSIONS: This study proposes and evaluates a novel approach for assessing baseline demographic covariates potentially affecting VE. Results show that the proposed approach can sensitively and specifically identify potentially important covariates and provides a method for evaluating their likely clinical significance in terms of predicted impact on vaccine efficacy. It shows further that inclusion of CoP data can enable more precise VE estimation, thus enhancing study power and/or efficiency and providing even better information to support health policy and development decisions.
- MeSH
- Demography statistics & numerical data MeSH
- Clinical Trials, Phase III as Topic statistics & numerical data methods MeSH
- Humans MeSH
- Logistic Models MeSH
- Computer Simulation MeSH
- Randomized Controlled Trials as Topic statistics & numerical data methods MeSH
- Vaccine Efficacy * statistics & numerical data MeSH
- Vaccination statistics & numerical data methods MeSH
- Vaccines therapeutic use MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Classifying a measurable clinical outcome as a dichotomous variable often involves difficulty with borderline cases that could fairly be assigned either of the two binary class memberships. In such situations the indicated class membership is often highly subjective and subject to, for instance, a measurement error. In other situations the intermediate level of a three-level ordinal factor may sometimes be explicitly reserved for cases which could likely belong to either of the two binary classes. Such indefinite readings are often eliminated from the statistical analysis. In this article we review conceptual and methodological aspects of employing proportional odds logistic regression for a three level ordinal factor as a suitable alternative to ordinary logistic regression when dealing with limited uncertainty in classifying clinical outcome as a binary variable. Copyright 2006 John Wiley & Sons, Ltd.
- MeSH
- Atherosclerosis ultrasonography MeSH
- Cholesterol blood MeSH
- Data Interpretation, Statistical * MeSH
- Smoking MeSH
- Blood Glucose metabolism MeSH
- Humans MeSH
- Logistic Models * MeSH
- Predictive Value of Tests MeSH
- Models, Statistical * MeSH
- Calcium blood MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Czech Republic MeSH
Quantitative applications in the social sciences ; 135
vii, 70 s. : il.
