Lifestyle Segmentation Variables as Predictors of Home-Based Trips for Atlanta, Georgia, Airport

2012 ◽  
Vol 2266 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Josephine D. Kressner ◽  
Laurie A. Garrow
Keyword(s):  
Regression Models ◽  
Travel Demand ◽  
Economic Factors ◽  
Linear Regression Models ◽  
Data Set ◽  
Credit Reporting ◽  
Adequate Information ◽  
Home Based ◽  
Air Travel Demand ◽  
Socio Economic Factors

This research investigated the influence of demographic and socio-economic factors on air travel demand by using a unique data set purchased from a credit-reporting agency. Linear regression models based on lifestyle segmentation variables were used to predict air passenger trips for Hartsfield–Jackson International Airport in Atlanta, Georgia. The study focused on predicting trips that originated from or terminated at residences in Atlanta's 13-county metropolitan area. The lifestyle regression models were compared with regression models based on income, because the latter were similar to the regression models currently used by the Atlanta Regional Commission to predict home-based airport passenger trips. The results provide directional evidence for using lifestyle clusters over income groups in predicting airport passenger trips. The evidence suggests that alternative data sources with adequate information for lifestyle segmentation can improve airport passenger models. The discussion points out the need for air passenger surveys to collect information about the number of annual air trips a surveyed individual takes.

Challenging the Link Between Early Childhood Television Exposure and Later Attention Problems: A Multiverse Approach

2021 ◽  
pp. 095679762097165
Author(s):  
Matthew T. McBee ◽  
Rebecca J. Brand ◽  
Wallace E. Dixon
Keyword(s):  
Early Childhood ◽  
Regression Models ◽  
Propensity Score Analysis ◽  
Harmful Effect ◽  
Attention Problems ◽  
Linear Regression Models ◽  
Data Set ◽  
Logistic Regression Models ◽  
Score Analysis ◽  
Television Exposure

In 2004, Christakis and colleagues published an article in which they claimed that early childhood television exposure causes later attention problems, a claim that continues to be frequently promoted by the popular media. Using the same National Longitudinal Survey of Youth 1979 data set ( N = 2,108), we conducted two multiverse analyses to examine whether the finding reported by Christakis and colleagues was robust to different analytic choices. We evaluated 848 models, including logistic regression models, linear regression models, and two forms of propensity-score analysis. If the claim were true, we would expect most of the justifiable analyses to produce significant results in the predicted direction. However, only 166 models (19.6%) yielded a statistically significant relationship, and most of these employed questionable analytic choices. We concluded that these data do not provide compelling evidence of a harmful effect of TV exposure on attention.

scholarly journals Demographic and socio-economic factors affecting birth preparation and complication readiness (BPCR) practices in Nepal

2018 ◽  
Vol 18 (17) ◽  
pp. 23-32
Author(s):  
Sunil Kumar Acharya
Keyword(s):  
Economic Status ◽  
Economic Factors ◽  
Data Set ◽  
Complication Readiness ◽  
Factors Affecting ◽  
Logistics Regression ◽  
Analysis Technique ◽  
The Government ◽  
Status Of Women ◽  
Socio Economic Factors

BPCR practices by women in Nepal are still low. Still a relatively high percentage of women do not make BPCR to its fullest extent. Researches in developing countries show that various demographic, social and economic factors influence the BPCR practices by pregnant women. This paper examines the likelihood of BPCR practices based on women’s demographic, social and economic status in Nepal. NDHS 2011 data set has been utilized by applying bivariate logistics regression analysis technique to examine the effects of these variables on BPCR practices in Nepal. The analysis result shows high variations and gaps in BPCR practice based on demographic, social and economic status of women. Against this finding the study recommends implementation of appropriate policy and program measures by the government and other agencies to address the existing variations and gaps in BPCR practices among subgroups of women in Nepal. Further research studies focusing on the existing barriers on BPCR practice need to be conducted in Nepal especially among the women who are disadvantaged and marginalized.

scholarly journals Controlling the outcome of SN2 reactions in ionic liquids: from rational data set design to predictive linear regression models

2020 ◽  
Vol 22 (40) ◽  
pp. 23009-23018
Author(s):  
Alexandra Schindl ◽  
Rebecca R. Hawker ◽  
Karin S. Schaffarczyk McHale ◽  
Kenny T.-C. Liu ◽  
Daniel C. Morris ◽  
...  
Keyword(s):  
Ionic Liquids ◽  
Linear Regression ◽  
Regression Models ◽  
Reaction Rate ◽  
Computational Approach ◽  
Set Design ◽  
Linear Regression Models ◽  
Data Set ◽  
Reaction Rate Constants ◽  
Sn2 Reactions

An iterative, combined experimental and computational approach towards predicting reaction rate constants in ionic liquids is presented.

scholarly journals Ranking Significant Features for Increasing Engagement on Social Media via Regression Analysis

2021 ◽  
Author(s):  
Thiago R. C. de Lima
Keyword(s):  
Social Media ◽  
Regression Models ◽  
Model Fit ◽  
Short Paper ◽  
Media Content ◽  
Linear Regression Models ◽  
Data Set ◽  
Social Media Data ◽  
Unseen Data ◽  
Media Data

Social media comprises of platforms that surpassed their initial goal to connect people just for the sake of socializing and currently provide powerful tools for businesses to reach millions of views worldwide, increasing their chances of gaining new customers. This short paper utilizes the Buzz in Social Media data set available at UCI Machine Learning Repository for identifying the attributes in social media content that have the highest correlation to the amount of repercussion it gained. To achieve such result, several linear regression models are constructed, then ranked based on their respective model fit measure (R-squared) and accuracy when tested against unseen data.

Socio-economic Correlates of Marital Status and Marriage Timing Among Adult Men in Nigeria

2022 ◽  
pp. 0192513X2110598
Author(s):  
David A. Okunlola ◽  
Olusesan A. Makinde ◽  
Stella Babalola
Keyword(s):  
Regression Models ◽  
Cox Regression ◽  
Economic Factors ◽  
Adult Men ◽  
First Marriage ◽  
Age At First Marriage ◽  
Multiple Indicator Cluster Survey ◽  
Multiple Indicator ◽  
Wealth Group ◽  
Socio Economic Factors

There is a gradual tendency towards prolonged bachelorhood among men in Nigeria. Studies have linked this to socio-economic factors, but this evidence is sparsely explored in the context of Nigeria. Hence, this study fills the knowledge gap. The 2016/17 Nigeria Multiple Indicator Cluster Survey data of 7803 adult men (aged 18–34 years) was analysed by using descriptive and fitting binary logitic regression and Cox regression models. Results show that slightly more than one-third of adult men in Nigeria (35%) had a marriage history and their median age at first marriage was about 24 years. Educated men (than the uneducated) and those in middle wealth group (than the poor men) were less likely to have ever been married and to delay marriage, respectively. Wealthy men were more likely to delay marriage. Employed men were more likely to have a marriage history and to delay marriage.

Households’ financial vulnerability in Southern Europe

2018 ◽  
Vol 45 (3) ◽  
pp. 521-542
Author(s):  
Marco Terraneo
Keyword(s):  
Regression Models ◽  
Balance Sheet ◽  
Southern Europe ◽  
Linear Regression Models ◽  
Financial Vulnerability ◽  
Data Set ◽  
Content Type ◽  
Valuable Alternative ◽  
Vulnerable Families ◽  
Part Model

Purpose The purpose of this paper is to analyze whether and to what extent households living in southern Europe, i.e. Greece, Portugal, Spain and Italy, experience similar conditions of financial vulnerability, considering that in comparative research these countries are often grouped together because of the substantial instability of their economies and the similarity of social and welfare model. Design/methodology/approach The authors use data from Household Finance and Consumption Survey, a quite novel data set that covers the whole balance sheet of a sample of households. The authors compute four indicators of debt burden and in order to study households’ risk of default the authors apply two-part model, which is a valuable alternative to the application of conventional regression models with zero-inflated data. Findings Analysis reveals that the burden of debts and the risk of default are very different among the four countries, in particular Spain and Portugal have the highest proportion of financially vulnerable households. Originality/value The study is one a few that have directly compared objectives indicators of households’ financial vulnerability in all Southern European countries. Moreover, the authors employ a two-part model, a valuable alternative to the application of conventional logit or linear regression models. In the first part of the model the authors estimate the probability that households suffer financial vulnerability; in the second part, the authors estimate households’ level of vulnerability only for vulnerable families.

scholarly journals Breeding progress of disease resistance and impact of disease severity under natural infections in winter wheat variety trials

2021 ◽  
Vol 134 (5) ◽  
pp. 1281-1302 ◽  
Author(s):  
F. Laidig ◽  
T. Feike ◽  
S. Hadasch ◽  
D. Rentel ◽  
B. Klocke ◽  
...  
Keyword(s):  
Linear Regression ◽  
Winter Wheat ◽  
Disease Severity ◽  
Regression Models ◽  
Resistance Breeding ◽  
Linear Regression Models ◽  
Data Set ◽  
Variety Trials ◽  
Ageing Effects ◽  
Severity Scores

Abstract Key message Breeding progress of resistance to fungal wheat diseases and impact of disease severity on yield reduction in long-term variety trials under natural infection were estimated by mixed linear regression models. Abstract This study aimed at quantifying breeding progress achieved in resistance breeding towards varieties with higher yield and lower susceptibility for 6 major diseases, as well as estimating decreasing yields and increasing disease susceptibility of varieties due to ageing effects during the period 1983–2019. A further aim was the prediction of disease-related yield reductions during 2005–2019 by mixed linear regression models using disease severity scores as covariates. For yield and all diseases, overall progress of the fully treated intensity (I2) was considerably higher than for the intensity without fungicides and growth regulators (I1). The disease severity level was considerably reduced during the study period for mildew (MLD), tan spot (DTR) and Septoria nodorum blotch (ear) (SNB) and to a lesser extent for brown (leaf) rust (BNR) and Septoria tritici blotch (STB), however, not for yellow/stripe rust (YLR). Ageing effects increased susceptibility of varieties strongly for BNR and MLD, but were comparatively weak for SNB and DTR. Considerable yield reductions under high disease severity were predicted for STB (−6.6%), BNR (−6.5%) and yellow rust (YLR, −5.8%), but lower reductions for the other diseases. The reduction for resistant vs. highly susceptible varieties under high severity conditions was about halved for BNR and YLR, providing evidence of resistance breeding progress. The empirical evidence on the functional relations between disease severity, variety susceptibility and yield reductions based on a large-scale multiple-disease field trial data set in German winter wheat is an important contribution to the ongoing discussion on fungicide use and its environmental impact.

scholarly journals Dietary and socio-economic factors associated with overweight and obesity in a southern French population

2004 ◽  
Vol 7 (4) ◽  
pp. 513-522 ◽  
Author(s):  
J Scali ◽  
S Siari ◽  
P Grosclaude ◽  
M Gerber
Keyword(s):  
Tobacco Use ◽  
Population Sample ◽  
High Energy ◽  
Overweight And Obesity ◽  
Dietary Factors ◽  
Economic Factors ◽  
Linear Regression Models ◽  
Men And Women ◽  
Factors Associated ◽  
Socio Economic Factors

AbstractObjective:To investigate the socio-economic and dietary factors associated with overweight and obesity, respectively, in southern France.Design:Cross-sectional analysis of socio-economic, lifestyle and nutritional characteristics of a representative population sample. A questionnaire elicited information on anthropometric measurements, socio-economic factors, physical activity, tobacco use, and alcohol and food intakes. Non-parametric tests, multiple linear regression models and correspondence factorial analysis (CFA) were used to estimate the association of the various factors with overweight and obesity.Setting:French Southwest and Mediterranean areas.Subjects:In total, 1169 subjects (578 women and 552 men), aged 30–77 years, were recruited at random.Results:Overweight and obesity were associated with age and education in both genders, reproductive factors in women and tobacco use in men. A few dietary factors were identified (high energy intake and low intake of carbohydrates), but all these variables explained little of the variation (18.5% in women and 14.6% in men). The CFA further investigated the association of lifestyle and nutritional factors, giving more weight to nutritional behaviour for overweight men and women. Factors for obesity differed from those for overweight by being different in men and women, possibly related to psychological behaviour, and there were fewer of them, suggesting an insufficient coverage by the usual questionnaires.Conclusions:Overweight and obesity appear as two different entities. Energy imbalance induced by various lifestyle factors plays a major role in the development of overweight, whereas obesity represents a more complex entity where psychological and genetic factors that are difficult to assess may be more important. General nutritional guidelines appear more adapted to the prevention of overweight than to that of obesity, and individual counselling to the prevention of obesity.

scholarly journals Forecasting of Tourism Processes in Uzbekistan in Correlation Regression Models

2021 ◽  
Vol 04 (11) ◽  
Author(s):  
F.E. Gulmurodov ◽  
Keyword(s):  
Future Development ◽  
Regression Models ◽  
Regression Function ◽  
Development Trend ◽  
Tourism Industry ◽  
Economic Factors ◽  
Factors Affecting ◽  
The Future ◽  
The Development Trend ◽  
Socio Economic Factors

The article provides detailed information on the process of developing effective plans for the development of the tourism industry and choosing the optimal one based on them, forecasting the future development of the industry. It also considers the processes of using special computational and arithmetic methods that allow predicting the events and happenings in the tourism industry, to determine the regression function as a result of the interaction and interaction of indicators representing the type of activity. As a result of targeted research, using correlation-regression models, a forecast of the development trend of the tourism industry based on socio-economic factors affecting the tourism process was developed.

scholarly journals Impact of Income, Density, and Population Size on PM2.5 Pollutions: A Scaling Analysis of 254 Large Cities in Six Developed Countries

2021 ◽  
Vol 18 (17) ◽  
pp. 9019
Author(s):  
Moon-Jung Kim ◽  
Yu-Sang Chang ◽  
Su-Min Kim
Keyword(s):  
Population Size ◽  
National Policy ◽  
Urban Pollution ◽  
Developed Countries ◽  
Scaling Analysis ◽  
Economic Factors ◽  
Stirpat Model ◽  
Data Set ◽  
The Impact ◽  
Socio Economic Factors

Despite numerous studies on multiple socio-economic factors influencing urban PM2.5 pollution in China, only a few comparable studies have focused on developed countries. We analyzed the impact of three major socio-economic factors (i.e., income per capita, population density, and population size of a city) on PM2.5 concentrations for 254 cities from six developed countries. We used the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model with three separate data sets covering the period of 2001 to 2013. Each data set of 254 cities were further categorized into five subgroups of cities ranked by variable levels of income, density, and population. The results from the multivariate panel regression revealed a wide variation of coefficients. The most consistent results came from the six income coefficients, all of which met the statistical test of significance. All income coefficients except one carried negative signs, supporting the applicability of the environmental Kuznet curve. In contrast, the five density coefficients produced statistically significant positive signs, supporting the results from previous studies. However, we discovered an interesting U-shaped distribution of density coefficients across the six subgroups of cities, which may be unique to developed countries with urban pollution. The results from the population coefficients were not conclusive, which is similar to the results of previous studies. Implications from the results of this study for urban and national policy makers are discussed.

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