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Every season, the international football calendar contains periods reserved for a series of so-called special matches including international friendly or official matches played by national teams. All these periods are called "FIFA reserved dates" in the calendar and generally cause domestic competitions to shut down for some days. Such interruptions of leagues might generate different effects or externalities on the performance of players, clubs or even on the competition itself. While a number of studies focus on the effect of players' absence on teams performance, this paper contributes to the literature by specifically evaluating the effect of the "FIFA reserved dates" on football outcomes. Using data for four seasons from the "big five" European leagues, I find evidence that the outcomes distribution in those matches played after a "FIFA reserved date" becomes more balanced, thus increasing the uncertainty of outcome. The results of this paper contribute to the ongoing debate about the management and design of the international match calendar. [ABSTRACT FROM AUTHOR]

Transformative rural smallholder agriculture addressing biophysical constraints requires farmer-led innovations for increased technology adoption. Following the need to further intensify the cereal push-pull technology (PPT) for pest and weed control through integration with vegetables, we conducted surveys to determine suitable vegetables across three different counties in Kenya namely Homabay, Siaya, and Trans-Nzoia. Farming in these areas is predominantly maize based and introducing vegetables to the system will improve household food and nutritional security, together with income. A systematic random sampling method was used to sample a total of 124 smallholder farmers who were interviewed during field days where farmer awareness of technology was mobilised. Descriptive results showed kale (47%) and black nightshade (30%) were the most preferred high-value vegetables for integration into push-pull plots in these regions. This was followed by cowpea (15%), onion (6%) and tomato (2%) underlying the wide range of farmer preferences and priorities. There were also gender differences in vegetable preferences with men preferring capital intensive and high value vegetables, while women preferred traditional vegetables. Results from multinomial logit model revealed that age of the farmer, education level, gender, and farming experience were determinants of smallholder farmers' vegetable preferences for integration into the PPT plots. The contribution of these determinants to farmer preferences varied across different vegetables where the contribution of the market value of the vegetable in selection diminished with while the effect of farmers' age. Overall, male farmers preferred capital-intensive and market-oriented vegetables in compared to their female counterparts. We conclude that gender and geographic location play a pivotal role in farmers' crop selection for integration in the PPT. Targeted awareness creation pathways accounting for these factors will be critical for wider adoption of the PPT. [ABSTRACT FROM AUTHOR]

Traffic collision on the highway has become a serious issue because they delay the sustainable development of society. Highway accidents on I-69 have one of the highest crash rates in the U.S. in Michigan and a higher fatality rate than average. This study aimed to identify the factors contributing to highway user crashes on I-69 in Flint, Michigan. This study also aims to find the relationship between the change in the speed limit in certain weather conditions and the number of crashes that are likely to occur as a sequence of this change. A multi-user driving simulator is applied in this study with ten driving simulator scenarios. The multinomial logistic regression (MNL) model has been investigated in this study to identify statistically significant factors that contribute to crashes. The analyses indicated that speeding and speed limits during inclement road/weather conditions significantly affected crashes. Most frequent crashes occur during snowy and icy weather conditions and by changing the speed limit during these weather conditions. Therefore, driver speed is suspected to be the most factor that caused the Vehicle crash on I-69. The method of developing Crash modification factors (CMFs) used in this study will help to reduce crashes and improve traffic system safety performance on highways in Michigan. [ABSTRACT FROM AUTHOR]

Increasing body of health planning and policy research focused upon unravelling the fundamental drivers of population health and nutrition inequities, such as wealth status, educational status, caste/ethnicity, gender, place of residence, and geographical context, that often interact to produce health inequalities. However, very few studies have employed intersectional framework to explicitly demonstrate how intersecting dimensions of privilege, power, and resources form the burden of anthropometric failures of children among low-and-middle income countries including India. Data on 2,15,554 sampled children below 5 years of age from the National Family Health Survey 2015-2016 were analysed. This study employed intersectional approach to examine caste group inequalities in the anthropometric failure (i.e. moderate stunting, severe stunting, moderate underweight, severe underweight, moderate wasting, severe wasting) among children in India. Descriptive statistics and multinomial logistic regression models were fitted to investigate the heterogeneities in the burden of anthropometric failure across demographic, socioeconomic and contextual factors. Interaction effects were estimated to model the joint effects of socioeconomic position (household wealth, maternal education, urban/rural residence and geographical region) and caste groups with the likelihood of anthropometric failure among children. More than half of under-5 children suffered from anthropometric failure in India. Net of the demographic and socioeconomic characteristics, children from the disadvantageous caste groups whose mother were illiterate, belonged to economically poor households, resided in the rural areas, and coming from the central and eastern regions experienced disproportionately higher risk of anthropometric failure than their counterparts in India. Concerted policy processes must recognize the existing heterogeneities between and within population groups to improve the precision targeting of the beneficiary and enhance the efficiency of the nutritional program among under-5 children, particularly for the historically marginalized caste groups in India. [ABSTRACT FROM AUTHOR]

Dockless bike-sharing schemes have become more prevalent in cities around the world. While interest grows in studying their usage in larger cities with well-established transit systems, their role in expanding mid-sized cities that are more automobile-dependent remains understudied. This paper evaluates how dockless bike-sharing can provide a last-mile solution by connecting existing bus stops with destinations through an analysis of data collected from 111,155 unique trips over a five-month trial period of a dockless bike sharing scheme in Reno-Sparks, Nevada, USA in the summer of 2018. We classify trips by frequency of use and adjacency to bus stops into four categories and apply multivariate models to determine the link between trip occurrence and the immediate environment. Results indicate that repeat users' trips that begin or end near the same bus stop are more strongly linked with proximity to bicycle lanes, but tend to be farther from parks and casino resorts. Repeat users typically start trips in denser areas of the city with higher shares of renter-occupied units and higher shares of households without a vehicle, and ended them in less dense areas with higher percentages of white, non-Hispanic residents compared to one-time users. We find differences in association of travel with food service, education, health services, and casino resorts. This suggests that some benefits may be realized by deploying bike sharing in mid-sized cities, but must be weighed against findings that show repeat usage in areas that align with common early transportation technology and service adopter profiles. [ABSTRACT FROM AUTHOR]

This paper focuses on the aspect of road safety based on the impact of pavement conditions on accident severities. Four models of binomial and multinomial logistic regression were produced using the R software and utilizing two years of accident and pavement conditions data on Malaysian highways. The surface characteristics analyzed included the International Roughness Index (IRI), rut depth (RD) and mean texture depth (MTD). The accident severity assessed ranged from damage to death. Results indicated that IRI has the highest tendency to affect accident severity for all models. At the thresholds identified for all independent variables, the chances of death had increased significantly. As such, efforts must be driven to ensure the thresholds are not reached in the maintenance of pavements for better road safety. [ABSTRACT FROM AUTHOR]

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Abstract Background Utilisation of continuum of maternal health care services is crucial for a healthy pregnancy and childbirth and plays an important role in attaining Universal Health Coverage (UHC) and Sustainable Development Goals (SDGs) related to maternal and child health. This paper aims to assess the percentage of dropouts across various stages of utilization of continuum of maternal health services (CMHS) in India and also investigates the factors hindering the utilization of these services. Methods We used recent data from National Family Health Survey(NFHS) encompassing a total sample of 1,70,937 pregnant women for the period 2015–16. The percentage of women dropping out while seeking maternal health care is measured using descriptive statistics. While, the factors impeding the utilization of maternal health services is estimated using a Multinomial Logistic Regression Model, where dependent variable (CMHS) is defined as complete care, incomplete care and no care. Results Only17% of pregnant women availed the utilisation of complete care and 83% either did not seek any care or dropped after seeking one or two services. For instance, it is found that 79% of women who registered for antenatal care services (ANC) did not avail the same adequately. An empirical investigation of determinants of inadequate utilization of CMHS revealed that factors like individual characteristics, for instance- access to media (RRR: 2.06) and mother’s education play (RRR: 3.61) a vital role in the uptake of CMHS. It is also found that the interaction between wealth index and place of residence plays a pivotal role in seeking complete care. Lastly, the results revealed that male participation (RRR: 2.69) and contacting multi-purpose worker (MPW) (RRR: 2.33) are also at play. Conclusion The study suggests that the major determinants of utilisation of CMHS are access to media, mother’s education, affordability barriers and male participation. Hence, policy recommendations should be oriented towards strengthening these dimensions and the utilisation of adequate ANC has to be considered as the need of the hour.

Against the discourse according to which peripheral areas are places doomed to an inevitable fate of constant demographic decline, the aim of this paper is to contribute to the understanding of the local determinants fostering adaptability to prolonged challenges connected to a condition of peripherality. Exploiting the classification produced within the Italian National Strategy for Inner Areas, we investigate the determinants of positive demographic long‐term growth paths of Italian peripheral municipalities using population variation as the dependent variable in a spatially deep lagged model relying on census data from 1971 to 2011. Our longitudinal study provides evidence on the positive effects of demographic and labour‐skills related factors on population dynamics, that may prove to be crucial in supporting policymakers when formulating place‐sensitive strategies to enhance the adaptive capability of places in the face of long‐term slow‐burning pressures. [ABSTRACT FROM AUTHOR]

Energy plays a role of pivotal importance in our daily lives. It is an important requisite for all of us. The main objective of the study is to investigate what factors determine households’ preferences and choice of energy sources for heating in rural areas of District Peshawar, Pakistan. The study is based on a sample of 512 households, which were randomly selected. “To estimate the households” preferences for heating purposes, a multinomial logistic regression model was used”. A multiple linear regression model was also used to investigate factors affecting the cost of energy sources for heating. The results of the multinomial logistic regression model showed that age, gender, education level, and income of households’ head, distance from the nearest market and cost of heating had a positive relationship, while family size had a negative relationship with the use of electricity. Education level and income of household head and distance from the nearest market had significant effect on the use of electricity. In case of firewood, the coefficients of education level, income, and cost on heating of households showed positive, while the age and family size showed negative relationship. The results of the multiple linear regression model showed that the relationship of the education level of households head, age, income, family size, and distance from the the nearest market had positive, but negative relationship was found between the cost of heating and the use of natural gas in the study area. The study concluded that the income and education level of households had a more significant effect on the use of electricity, firewood, and natural gas than animal dung cakes. The study recommends that the government should ensure the availability of natural gas to be used for heating purposes in the study area. There was a severe shortage of electricity and natural gas; so “the government should take remedial measures in the studied area.” [ABSTRACT FROM AUTHOR]

Background: Utilisation of continuum of maternal health care services is crucial for a healthy pregnancy and childbirth and plays an important role in attaining Universal Health Coverage (UHC) and Sustainable Development Goals (SDGs) related to maternal and child health. This paper aims to assess the percentage of dropouts across various stages of utilization of continuum of maternal health services (CMHS) in India and also investigates the factors hindering the utilization of these services.Methods: We used recent data from National Family Health Survey(NFHS) encompassing a total sample of 1,70,937 pregnant women for the period 2015-16. The percentage of women dropping out while seeking maternal health care is measured using descriptive statistics. While, the factors impeding the utilization of maternal health services is estimated using a Multinomial Logistic Regression Model, where dependent variable (CMHS) is defined as complete care, incomplete care and no care.Results: Only17% of pregnant women availed the utilisation of complete care and 83% either did not seek any care or dropped after seeking one or two services. For instance, it is found that 79% of women who registered for antenatal care services (ANC) did not avail the same adequately. An empirical investigation of determinants of inadequate utilization of CMHS revealed that factors like individual characteristics, for instance- access to media (RRR: 2.06) and mother's education play (RRR: 3.61) a vital role in the uptake of CMHS. It is also found that the interaction between wealth index and place of residence plays a pivotal role in seeking complete care. Lastly, the results revealed that male participation (RRR: 2.69) and contacting multi-purpose worker (MPW) (RRR: 2.33) are also at play.Conclusion: The study suggests that the major determinants of utilisation of CMHS are access to media, mother's education, affordability barriers and male participation. Hence, policy recommendations should be oriented towards strengthening these dimensions and the utilisation of adequate ANC has to be considered as the need of the hour. [ABSTRACT FROM AUTHOR]

Robust estimators and Wald-type tests are developed for the multinomial logistic regression based on $\phi $ -divergence measures. We compute the influence function of the proposed estimators and tests and discuss some consequences. Their robustness is illustrated by an extensive simulation study and two real examples. [ABSTRACT FROM AUTHOR]

Regardless of the several measures implemented by the South African government to curb food insecurity, the majority of rural households are still experiencing food insecurity at a provincial level, particularly in Limpopo Province. This could be because of the high unemployment rate that is becoming worse in the province. Even though many members of rural households have obtained tertiary education, most individuals still find it difficult to secure jobs. This results in many rural households depending on social grants to make a living, and it is acknowledged that these social grants are not enough to provide for a household's food needs. This study was conducted in the Mopani district municipality. A multistage sampling procedure was used, and villages were selected based on probability proportionate to size, making a total of 173 rural households. The objectives of the study were to profile the socio-economic characteristics of rural households, to identify the food security status of these households, and to determine the factors that influence food security among them. To analyse the data, the Household Food Insecurity Access Scale, descriptive statistics and the multinomial logistic regression model were used. From the findings of the study, the results revealed that the majority of rural households in the Mopani district municipality were found to be moderately to severely food insecure as per logistic regression estimates. [ABSTRACT FROM AUTHOR]

Change-point detection in categorical time series has recently gained attention as statistical models incorporating change-points are common in practice, especially in the area of biomedicine. In this article, we propose a sequential change-point detection procedure based on the partial likelihood score process for the detection of changes in the coefficients of multinomial logistic regression model. The asymptotic results are presented under both the null of no change and the alternative of changes in coefficients. We carry out a Monte Carlo experiment to evaluate the empirical size of the proposed procedure as well as its average run length. We illustrate the method by using data on a DNA sequence. Monte Carlo experiments and real data analysis demonstrate the effectiveness of the proposed procedure.

In the last years, the agricultural systems based on Crop-Livestock-Forestry integration have emerged as a potential solution due to its capacity to maximize land use and reduces the effects of high temperatures on the animals. Within these systems, there exist an interest in technological solutions capable of monitor the animals in real-time. From this monitoring, one of the main interest is to know if an animal is in the sun or in the shade of a tree by using some environmental measures. However, as there is a possibility that the weather is cloudy, real-time monitoring also needs to identify this case. That is, the realtime monitoring also needs to differentiate the shade of a tree from a cloudy weather. The interest in this kind of monitoring is due to the fact that an animal that remains a long time under a shade of a tree provides substantial insights to indicate if this is in thermal stress. This information can be used in decision-making with the goal to reduce the impact of the thermal stress and consequently to provide welfare to the animal and reduces the financial losses. As a solution to identify if an animal is in the sun or in the shade of a tree or if the weather is cloudy, we developed an electronic device, used to capture values of environmental variables, which integrated with a mathematical model predicts the shade state (sun, shade or cloudy) where the animal can be found. We illustrate the performance of the proposed solution in a real data set.

Syntactic priming effects have been investigated for several decades in psycholinguistics and the cognitive sciences to understand the cognitive mechanisms that support language production and comprehension. The question of whether speakers prime themselves is central to adjudicating between two theories of syntactic priming, activation-based theories and expectation-based theories. However, there is a lack of a statistical model to investigate the two different theories when nominal repeated measures are obtained from multiple participants and items. This paper presents a Markov mixed-effect multinomial logistic regression model in which there are fixed and random effects for own-category lags and cross-category lags in a multivariate structure and there are category-specific crossed random effects (random person and item effects). The model is illustrated with experimental data that investigates the average and participant-specific deviations in syntactic self-priming effects. Results of the model suggest that evidence of self-priming is consistent with the predictions of activation-based theories. Accuracy of parameter estimates and precision is evaluated via a simulation study using Bayesian analysis. [ABSTRACT FROM AUTHOR]

Postharvest diseases and disorders are two of the most important parameters associated with the quality of avocado fruit. The aim of this study was to identify postharvest diseases and disorders in Hass avocado plots and to evaluate their relationships with different preharvest agronomical practices. This work was developed in 20 commercial plots of Hass avocado dedicated to production for national and export markets. The first part of this work was associated with the identification and characterization of diseases and disorders related with postharvest of avocado. In addition, it was determined the incidence of each disease and disorder based on simulation of postharvest scenarios for the national and export markets. Using a multinomial logistic regression model, it was possible to determine that the presence of each disease and disorder were related to crop management practices, soil and leaf and fruit nutrients levels. Most relevant postharvest disease and disorders were anthracnose, stem-end rot, chilling injury, and lenticel damage. Additionally, variables such as dry matter, plant pruning, and tissue concentration of Ca+2 were related with some pathologies and disorders. This work presents an advance in the recognition of postharvest diseases and disorders in avocado under tropical conditions, besides determining the main edaphic and anthropogenic associated factors.

Summary: The Software plagiarism, which arises the problem of software piracy is a growing major concern nowadays. It is a serious risk to the software industry that gives huge economic damages every year. The customers may develop a modified version of the original software in other types of programming languages. Furthermore, the plagiarism detection in different types of source codes is a challenging task because each source code may have specific syntax rules. In this paper, we proposed a methodology for software plagiarism detection in multiprogramming languages based on machine learning approaches. The Principal Component Analysis (PCA) is applied for features extraction from source codes without losing the actual information. It extracts features by factor analysis and converts the dataset into normalized linear principal components which are further useful for predictions analysis. Then, the multinomial logistic regression model (MLR) is applied to these components to classify the source codes documents based on predictions. It gives the generalization of logistic regression to handle multiclass problems. Further, the predictors' performance in MLR is evaluated by 2 tailed z test. To apply the experiment, the dataset is collected in five different and popular languages, ie, C, C++, Java, C#, and Python. Each programming language taken in two different case studies, ie, binary search and Stack. [ABSTRACT FROM AUTHOR]

Context: The investigation includes the Ecuadorian pharmaceutical market from the business competitive point of view, focusing on the service offered by the Farmacias Cruz Azul and Farmacias Económicas franchises. Aims: To determine the main variables that influence the consumer’s choice, in the pharmaceutical sector, and generate a predictive model of competitive analysis. Methods: Through the Multinomial Logistic Regression Model was obtained the significant variables that helped to predict the customer’s pharmacy choice. The study was conducted in the zone number 3, center of the country, whose sample was calculated based on the infinite population formula. A total of 393 clients were surveyed in the main public and private health centers. Results: After establishing the dependent variable “Choice of Pharmacy”; Of the eight variables under study, the relevant independent variables were: Reason for choice, Fidelity and Low prices with a level of significance of 0.003, 0.001 and 0.000, respectively. Final variables and introduced to the regression model; which resulted, a probability of 16.82% that customers go to Farmacias Cruz Azul, 77.12% to choose Farmacias Económicas, and only 0.63% to go to another pharmacy, including independent. Conclusions: The Multinomial Logistic Regression Model was useful to predict the probability of choice of pharmacy, which a client has according to variables that represent service; allowing also to perform key simulations for the continuous improvement of the sector, planning and competitive analysis.

Energy sources for cooking play an important role in rural areas of Pakistan. Unfortunately, most of the farm households in Pakistan still use traditional energy sources for cooking. The present study is based on choice of alternative energy sources for cooking in rural areas of district Peshawar. The main objective of the study was to examine what factors determine the choice of alternative energy sources of farm households for cooking in the study area. Primary data were collected from 512 farm households using simple random sampling. The sample size constituted 5 percent of total farm households in selected villages of district Peshawar. A multinomial logistic regression model was used for choice of alternative energy sources for cooking purpose. Results of the study showed that income of farm households, education level of household head and cooking cost had significant effect on the use of electricity, firewood and natural gas as against animal dung cakes for cooking purpose which was the base category. The study concluded that the use natural gas for cooking was less expensive as compared to other alternative energy sources such as firewood, electricity and animal dung cakes. The study also concluded that family size has negative relationship with the use electricity and firewood while income of farm households and education level of household head has positive relationship with the use of electricity, natural gas and firewood for cooking purpose in the study area. The study recommended that the government should provide natural gas in rural areas of Peshawar. Sample farm households facing problem regarding severe shortage of natural gas in the study area. So the concerned departments should take remedial measure to address the concerns needs of the rural people. [ABSTRACT FROM AUTHOR]

Over the past two decades, the Kingdom of Bahrain has seen an increase in weight in a growing number of people. Experts in the field determined that it is due to a lack of exercise and poor dietary habits. The problem has reached extremely high records of obesity, 37% for females and 30% for males. The statistics were gathered during a cross-sectional study of 414 volunteers at a local shopping mall. The goal of this primary data analysis was to determine the current prevalence of weight abnormalities. The findings were used to predict the trend of such abnormalities among the sub-population of university students in the Kingdom of Bahrain. The analysis, done in 2018, was based on 1,100 surveys collected from subjects in the central universities in the Kingdom of Bahrain including the University of Bahrain, Ahlia University, University of Bahrain, Arabian Gulf University, Applied Science University, AMM International University, Arab Open University, and Bahrain Polytechnic. The research was conducted over a period of several months. The authors developed surveys in both Arabic and English and randomly selected participants aged between eighteen and twenty-five among full-time students from major universities in the Kingdom of Bahrain. The standards of these weight issues, including obesity, over-weight and under-weight issues were determined with the use of the International Classification Index, a diagnostic tool used for health management. It is managed and prescribed by the World Health Organization. Based on the Chi-square test, the results strongly depended on gender, age, and the extent of exercise performed on a weekly basis. As a result, a multinomial logistic regression involving these factors was used to compare the trends and predict the highest and lowest probabilities of each abnormal weight. For example, the male students, aged twenty-two or older who exercised less than one hour a week, were the most likely to be obese and overweight; about a 20% to 42% chance, respectively. However, the students most probable to be underweight (about 11%) were those females; aged eighteen and nineteen who exercised less than one hour a week. [ABSTRACT FROM AUTHOR]

Mortality study is an essential component of actuarial risk management for life insurance policies, annuities, and pension plans. Life expectancy has drastically increased over the last several decades; consequently, longevity risk associated with annuity products and pension systems has emerged as a crucial issue. Among the various aspects of mortality study, a consideration of the cause-of-death mortality can provide a more comprehensive understanding of the nature of mortality/longevity risk. In this case study, the cause-of-mortality data in Korea and the US were analyzed along with a multinomial logistic regression model that was constructed to quantify the impact of mortality reduction in a specific cause on actuarial values. The results of analyses imply that mortality improvement due to a specific cause should be carefully monitored and reflected in mortality/longevity risk management. It was also confirmed that multinomial logistic regression model is a useful tool for analyzing cause-of-death mortality for actuarial applications. [ABSTRACT FROM AUTHOR]

Statistical matching is a method of combining multiple sources of data that are extracted or surveyed from the same population. It can be used in situation when variables of interest are not jointly observed. It is a low-cost way to expect high-effects in terms of being able to create synthetic data using existing sources. In this paper, we propose the several statistical micro matching methods using a multinomial logistic regression model when all variables of interest are categorical or categorized ones, which is common in sample survey. Under conditional independence assumption (CIA), a mixed statistical matching method, which is useful when auxiliary information is not available, is proposed. We also propose a statistical matching method with auxiliary information that reduces the bias of the conventional matching methods suggested under CIA. Through a simulation study, proposed micro matching methods and conventional ones are compared. Simulation study shows that suggested matching methods outperform the existing ones especially when CIA does not hold. [ABSTRACT FROM AUTHOR]

Vision-based technologies have been extensively applied for on-street parking space sensing, aiming at providing timely and accurate information for drivers and improving daily travel convenience. However, it faces great challenges as a partial visualization regularly occurs owing to occlusion from static or dynamic objects or a limited perspective of camera. This paper presents an imagery-based framework to infer parking space status by generating 3D bounding box of the vehicle. A specially designed convolutional neural network based on ResNet and feature pyramid network is proposed to overcome challenges from partial visualization and occlusion. It predicts 3D box candidates on multi-scale feature maps with five different 3D anchors, which generated by clustering diverse scales of ground truth box according to different vehicle templates in the source data set. Subsequently, vehicle distribution map is constructed jointly from the coordinates of vehicle box and artificially segmented parking spaces, where the normative degree of parked vehicle is calculated by computing the intersection over union between vehicle's box and parking space edge. In space status inference, to further eliminate mutual vehicle interference, three adjacent spaces are combined into one unit and then a multinomial logistic regression model is trained to refine the status of the unit. Experiments on KITTI benchmark and Shanghai road show that the proposed method outperforms most monocular approaches in 3D box regression and achieves satisfactory accuracy in space status inference. [ABSTRACT FROM AUTHOR]

Since 2003, Brazil has striven to provide energy access to all, in rural areas, in an effort to economically empower the communities. Unpacking fuel stacking behaviour can shed light onto the speed of transition toward the exclusive use of advanced fuel types. This paper presents findings from surveys that were carried out with 14 non-electrified communities in a rural area of Rio Negro, Amazonas State, Brazil. We identify the fuel choice determinants in these communities using a multinomial logistic regression model and more generally discuss the validity and robustness of such models in the context of statistical validation and evaluation metrics. Specifically for the Amazonas communities considered in this study, the research showed that the fuel choice determinants are the age of household, the number of people at meals each day, the number of meals daily, the community, education of the household head, and the income level of the household. Moreover, given the Brazilian policies related to energy and sustainability, this region is not likely to reach the Sustainable Development Goals proposed by United Nations for 2030.