scholarly journals PEMODELAN ANGKA KEMATIAN KECELAKAAN LALU LINTAS DI KOTA DENPASAR

2021 ◽  
Vol 10 (2) ◽  
pp. 111
Author(s):  
NI LUH WIWIN YUNIARTI ◽  
I GUSTI AYU MADE SRINADI ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Traffic Congestion ◽  
Poisson Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Poisson Regression Model ◽  
Chi Square ◽  
Poisson Regression Analysis ◽  
Accident Data

Denpasar City is one of the most crowded areas on the island of Bali, this is due to the fast population growth rate. This fast population can cause problems, one of the problem is in the transportation sector. The increase in the volume of transportation can cause traffic congestion which can lead to a high number of traffic accidents, this can lead to death due to traffic accidents in Denpasar City. To determine the factors that influence traffic accident mortality, researchers used Poisson regression analysis. Based on data on traffic accidents in Denpasar City in 2018, the deviance value is smaller than the chi square value. Therefore Poisson regression analysis is sufficient to model traffic accident data in Denpasar City. The Poisson regression model obtained from this research is. Based on the Poisson regression model obtained, the independent variable that contributes significantly and has a high effect on the number of people who die in traffic accidents is the driver factor.

scholarly journals PENERAPAN REGRESI BINOMIAL NEGATIF UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON

2013 ◽  
Vol 2 (2) ◽  
pp. 6
Author(s):  
PUTU SUSAN PRADAWATI ◽  
KOMANG GDE SUKARSA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Poisson Regression Model ◽  
Response Variable ◽  
Poisson Regression Analysis ◽  
Binomial Regression ◽  
The Mean

Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From the simulation data which experienced overdispersion in the Poisson Regression model it was found that the Negative Binomial Regression was better than the Poisson Regression model.

scholarly journals Factors Influencing the Frequency of Consumers’ Purchases of Locally-Produced Rice in Indonesia: A Poisson Regression Analysis

Agriculture ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 117 ◽  
Author(s):  
Apri Wahyudi ◽  
John K. M. Kuwornu ◽  
Endro Gunawan ◽  
Avishek Datta ◽  
Loc T. Nguyen
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Marketing Strategy ◽  
Poisson Regression ◽  
Poisson Regression Model ◽  
Poisson Regression Analysis ◽  
Empirical Results ◽  
Factors Influencing ◽  
Using Data

This study assessed the factors influencing the frequency of purchases of locally-produced rice using data collected from a sample of 400 consumers in Jakarta Province in Indonesia. The empirical results of a Poisson regression model revealed that socio-economic characteristics of the consumers (i.e., gender, age, occupation, education, and income), characteristics of the product (i.e., label and color), and the product’s price and promotion significantly influenced consumers’ frequency of purchasing locally-produced rice. The implication is that increasing the quality of locally-produced rice, applying an appropriate marketing strategy such as offering a relatively lower-priced product compared to the price of imported rice, and product promotion are necessary for increasing the frequency of consumers’ purchases of locally-produced rice.

Poisson Regression Analysis of Highway Fatality Accident Data in Oklahoma

2014 ◽  
Vol 5 (4) ◽  
pp. 72-86
Author(s):  
Jonathan C. Comer ◽  
Nicholas J. Rose ◽  
Leonard S. Bombom
Keyword(s):  
Regression Analysis ◽  
Rural Areas ◽  
Poisson Regression ◽  
Department Of Transportation ◽  
Highway Design ◽  
Crash Data ◽  
Poisson Regression Analysis ◽  
Accident Data ◽  
Oklahoma Department Of Transportation ◽  
Construction Practice

Analysis of fatality automobile accident data can be challenging in rural areas where a relatively small number of such accidents occurs on specific sections of highways. Combining crash data from the Fatality Analysis Reporting System (FARS) and highway networks and design specifications from the Oklahoma Department of Transportation (ODOT), this article employs Poisson regression analysis to determine what roadway characteristics (e.g. grade, geometry, and design) are most associated with fatal accidents on predominantly rural segments of highways in Oklahoma. The results provide information about what combinations of highway design traits have contributed most to past crashes and therefore can identify potentially dangerous road segments system-wide. This information will help transportation engineers evaluate current construction practice and seek ways to address design issues that are shown to contribute significantly to serious crashes.

scholarly journals Factors associated with breakfasting in users of a public health service

2013 ◽  
Vol 26 (2) ◽  
pp. 195-203 ◽  
Author(s):  
Patrícia Pinheiro de Freitas ◽  
Raquel de Deus Mendonça ◽  
Aline Cristine Souza Lopes
Keyword(s):  
Public Health ◽  
Health Promotion ◽  
Regression Analysis ◽  
Poisson Regression ◽  
Dietary Habits ◽  
Statistical Treatment ◽  
Cross Sectional Study ◽  
Chi Square ◽  
Cross Sectional ◽  
Poisson Regression Analysis

OBJECTIVE: This study identified the sociodemographic, lifestyle, dietary and anthropometric factors of users of a public health promotion service who have breakfast regularly. METHODS: This cross-sectional study included all users aged 20 years or more who joined the service between March 2007 and December 2010. Their socioeconomic and anthropometric data, dietary habits and health status were investigated. Statistical treatment included the Chi-square, Mann-Whitney, Fisher's exact and Student's t tests and Poisson regression analysis (p<0.05). RESULTS: Most of the participants (87.1%, n=528) breakfasted often, especially those aged 48.5 years or more (p=0.041). Poisson regression analysis showed association between breakfasting often and not smoking (PR=1.45, 95%CI: 1.10-1.91), having a greater number of daily meals (PR=1.15, 95%CI: 1.06-1.25), appropriate intake of deep-fried foods (PR=1.12, 95%CI: 1.01-1.25), lower fat intake (PR=0.78, 95%CI: 0.68-0.89) and smaller prevalence of excess weight (PR=0.85, 95%CI: 0.78-0.92). CONCLUSION: The positive relationship found between breakfasting often and not smoking, appropriate food and nutrient intakes and a healthier body weight shows the need of emphasizing this meal in health services as a simple and doable health promotion strategy that helps to prevent and control chronic diseases.

Poisson Regression Analysis of Highway Fatality Accident Data in Oklahoma

2018 ◽  
pp. 429-444
Author(s):  
Jonathan C. Comer ◽  
Nicholas J. Rose ◽  
Leonard S. Bombom
Keyword(s):  
Regression Analysis ◽  
Rural Areas ◽  
Poisson Regression ◽  
Department Of Transportation ◽  
Highway Design ◽  
Crash Data ◽  
Poisson Regression Analysis ◽  
Highway Networks ◽  
Accident Data ◽  
Oklahoma Department Of Transportation

Analysis of fatality automobile accident data can be challenging in rural areas where a relatively small number of such accidents occurs on specific sections of highways. Combining crash data from the Fatality Analysis Reporting System (FARS) and highway networks and design specifications from the Oklahoma Department of Transportation (ODOT), this article employs Poisson regression analysis to determine what roadway characteristics (e.g. grade, geometry, and design) are most associated with fatal accidents on predominantly rural segments of highways in Oklahoma. The results provide information about what combinations of highway design traits have contributed most to past crashes and therefore can identify potentially dangerous road segments system-wide. This information will help transportation engineers evaluate current construction practice and seek ways to address design issues that are shown to contribute significantly to serious crashes.

scholarly journals Faktor yang Berhubungan dengan Kecelakaan Lalu Lintas Pengemudi Sepeda Motor di Sumba Barat

2019 ◽  
Vol 1 (4) ◽  
pp. 170-176
Author(s):  
Oktavianus Lede Ngongo ◽  
Noorce C. Berek ◽  
Anna Heny Talahatu
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Human Life ◽  
Cross Sectional Study ◽  
The West ◽  
Chi Square ◽  
Cross Sectional ◽  
Factors Associated ◽  
Study Approach ◽  
Accident Data

Transportation is a very important part of human life. In fact, the existence of a vehicle can improve all human activities in carrying out the occupied routine, but problems arise such as traffic jams and increased traffic accidents. The Minister of Transportation stated that 4 (four) people died every day due to traffic accidents, 72% occurred in Indonesia and 73.9% involved motorbikes. The incident was an indication that motorbikes were the main cause of traffic accidents and contributed the most victims. The traffic accident data of the East Nusa Tenggara POLDA in 2018 showed a trend of increasing traffic accidents in 2018. The level of population mobilization in the legal territory of the West Sumba POLRES is relatively high. Based on the annual report book of West Sumba POLRES in 2018 there have been traffic accidents with 126 cases. The study aims to determine the factors associated with motorcycle driver traffic accidents in the area of West Sumba POLRES in 2019. This type of research is a quantitative study using a cross-sectional study approach. This research was conducted at the West Sumba Police Resort with a sample of 53 people. Analysis of the data used the chi-square statistical test. The results of this study indicate that the factors associated with traffic accidents are age (p = 0.004), behavior (p = 0.008), and lamp conditions (p = 0.005).

scholarly journals The Association of Coal-Fired Power Plants and the Mortality of COVID-19

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Alexander Irwin ◽  
Robert Hodgson
Keyword(s):  
Air Pollution ◽  
Regression Analysis ◽  
Mortality Rate ◽  
Poisson Regression ◽  
Power Plants ◽  
Energy Information Administration ◽  
Chi Square ◽  
Poisson Regression Analysis ◽  
Confounding Variables ◽  
Chi Square Test

With the outbreak and spread of severe acute respiratory syndrome coronavirus 2 (COVID-19), the world was ushered into a global pandemic. One of the most alarming aspects of the disease is the high mortality rate. Previous research has shown that long term exposure to air pollution has been correlated with the mortality rate of respiratory diseases. The focus of this study was to determine if there was any association between the mortality of COVID-19 and one of the primary producers of air pollution, coal-fired power plants. Using data from the US Energy Information Administration, John Hopkins University, and census department, a chi-square test and a poisson regression analysis were conducted to determine if living in proximity to coal-fired power plants had any effects on the mortality of COVID-19. The chi-square test results showed that there was no statistical significance as the variables showed independence. These results illustrate that there is no association between coal-fired power plants and the mortality rate of COVID-19. To expand on the results of the chi-square test, a poisson regression analysis was performed to account for the presence of confounding variables. This analysis showed similar results to the chi-square test, but due to issues with outliers in the data causing overdispersion, the model was unable to be accurately conducted, making all results inconclusive. With the inconclusive results of the poisson regression analysis, the conclusions drawn from the chi-square test were not able to be generalized as they were not verified in the presence of confounding variables. 

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