scholarly journals Multiple Linear Regression Model of Meningococcal Disease in Ukraine: 1992–2015

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Hennadii Mokhort
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Meningococcal Disease ◽  
Total Population ◽  
Linear Models ◽  
National Level ◽  
Multiple Linear Regression Model ◽  
National Database ◽  
Linear Regression Models ◽  
Susceptible Population

Estimating the rates of invasive meningococcal disease (IMD) from epidemiologic data remains critical for making public health decisions. In Ukraine, such estimations have not been performed. We used epidemiological data to develop a national database. These data were used to estimate the population susceptible to IMD and identify the prevalence of asymptomatic carriers of N. meningitidis using simple epidemiological models of meningococcal disease that may be used by the national policy makers. The goal was to create simple, easily understood analysis of patterns of the infection within Ukraine that would capture the major features of the infection dynamics. Studies used nationally reported data during 1992–2015. A logic model identified the prevalence of carriage and the proportion of the population susceptible to IMD as key drivers of IMD incidence. Multiple linear regression models for all ages (total population) and for children ≤14 years old were fit to national-level data. Linear models with the incidence of IMD as an outcome were highly associated with carriage and estimated susceptible population in both total population and children (R2 = 0.994 and R2 = 0.978, respectively). The susceptibility rate to IMD in the study total population averaged 0.0034 ± 0.0009% annually. At the national level, IMD can be characterized by the simple interaction between the prevalence of asymptomatic carriage and the proportion of the susceptible population. IMD association with prevalence rates of carriage and the proportion of susceptible population is sufficiently strong for national-level planning of intervention strategies for IMD.

scholarly journals Pedestrian crossing model in urban street (Case study on commercial area in Bandung)

2018 ◽  
Vol 181 ◽  
pp. 02004
Author(s):  
Sony Sulaksono Wibowo ◽  
Rian Wicaksana
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Group Size ◽  
Multiple Linear Regression Model ◽  
Delay Model ◽  
Affecting Factors ◽  
Linear Regression Models ◽  
Urban Street ◽  
Commercial Area ◽  
Pedestrian Crossing

Pedestrians who cross without any crossing facilities and under mixed-traffic tend to have varying responses. The responses can be analyzed by using multiple linear regression model, with pedestrian crossing delay and pedestrian crossing speed set as response variables. This research aims to develop two pedestrian crossing models based on the condition at the midblock part of urban street, in particular commercial area and without specific crossing facilities. The two models are pedestrian crossing delay model and pedestrian crossing speed model. The affecting factors are considered in linear relationship and the multiple-linear regression models are used. The principal factor in the pedestrian crossing delay model is group size of more than 3 persons, while in the model of pedestrian crossing speed, the principal factors are number of group size and pedestrian baggage. The mean of pedestrian crossing delay was about 3 seconds while pedestrian crossing speed was about 1 m/s.

scholarly journals Fear of falling in older adults living at home: associated factors

2017 ◽  
Vol 51 (0) ◽  
Author(s):  
Luciano Magalhães Vitorino ◽  
Carla Araujo Bastos Teixeira ◽  
Eliandra Laís Vilas Boas ◽  
Rúbia Lopes Pereira ◽  
Naiana Oliveira dos Santos ◽  
...  
Keyword(s):  
Older Adults ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Older Adult ◽  
Linear Models ◽  
Fear Of Falling ◽  
Multiple Linear Regression Model ◽  
Cross Sectional ◽  
Modifiable Factors ◽  
At Home

Abstract OBJECTIVE To identify the factors associated with the fear of falling in the older adultliving at home. METHOD Cross-sectional study with probabilistic sampling of older adultenrolled in two Family Health Strategies (FHS). The fear of falling was measured by the Brazilian version of the Falls Efficacy Scale-International and by a household questionnairethat contained the explanatory variables. Multiple Linear Regression using the stepwise selection technique and the Generalized Linear Models were used in the statistical analyses. RESULTS A total of170 older adultsparticipated in the research, 85 from each FHS. The majority (57.1%) aged between 60 and 69; 67.6% were female; 46.1% fell once in the last year. The majority of the older adults(66.5%) had highfear of falling. In the final multiple linear regression model, it was identified that a higher number of previous falls, female gender, older age, and worse health self-assessment explained 37% of the fear of falling among the older adult. CONCLUSION The findings reinforce the need to assess the fear of falling among the older adultliving at home, in conjunction with the development and use ofstrategies based on modifiable factors by professionalsto reduce falls and improve health status, which may contribute to the reduction of the fear of falling among the older adult.

scholarly journals Um modelo alternativo de risco para companhias não-financeiras aplicado ao setor brasileiro de papel e celulose

2009 ◽  
Vol 7 (3) ◽  
pp. 347
Author(s):  
Hsia Hua Sheng ◽  
Cristiane Karcher ◽  
Paulo Hubert Jr.
Keyword(s):  
At Risk ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Financial Risk ◽  
Risk Measure ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Linear Regression Models ◽  
Explanatory Factors ◽  
Paper And Pulp

Earnings at Risk (EaR) is a financial risk measure that can be applied to non-financial companies, similarly to Cash Flow at Risk (CFaR). It is based on a relation that can be quantified using a multiple linear regression model, where the dependent variable is the change on the company's results and the independent variables are changes in distinct risk factors. The presence of correlation between explanatory factors (multicollinearity) in this kind of model may cause problems when calculating EaR and CFaR. In this paper, we indicate some possible consequences of these problems when calculating EaR, and propose a method to solve it based on Principal Component Analysis technique. To test the model, we choose the Brazilian agriculture-business industry, more specifically the paper and pulp sectors. We will show that, on the absence of significant correlation between variables, the proposed model has equivalent performance to usual multiple linear regression models. We find evidence that when correlation appears, the model here proposed yields more accurate and reliable forecasts.

scholarly journals Analisis belanja daerah dan faktor-faktor yang mempengaruhinya di Kabupaten Bungo

2021 ◽  
Vol 10 (2) ◽  
pp. 135-146
Author(s):  
Pebrian Rahmad Ramadhan ◽  
Etik Umiyati
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Total Population ◽  
Multiple Linear Regression Model ◽  
Secondary Data ◽  
Population Balance ◽  
Considerable Effect

This study aims to: 1) analyze and describe the development of PAD, Balanced Fund, GRDP, population, HDI, and Regional Expenditure of Bungo Regency during 2004-2019. 2) To analyze the effect of PAD, Balanced Fund, GRDP, population, and HDI on Regional Expenditure of Bungo Regency during 2004-2019. The data used in this research is secondary data. The model used in this study is a multiple linear regression model. The results showed that partially the population, balance funds, population, and HDI had a significant effect on Regional Expenditures in Bungo Regency during 2004-2019, while PAD had no considerable effect on Regional Expenditures in Bungo Regency during 2004-2019 with a significant value of P < 0.05. The R2 value in this study was 0.987391. This shows that 98.7391% of regional expenditure in the Bungi Regency is influenced by the population, PAD, balancing funds, GRDP, and IPM. Meanwhile, 1.2609% were influenced by other factors that were not observed in this study. Keywords : Total population, PAD, Balancing fund, PDRB, IPM,  Regional expenditure

scholarly journals Simulation of wheat biomass yield by thermal time, rainfall and nitrogen

2017 ◽  
Vol 21 (11) ◽  
pp. 763-768 ◽  
Author(s):  
Ana P. B. Trautmann ◽  
José A. G. da Silva ◽  
Manuel O. Binelo ◽  
Osmar B. Scremin ◽  
Ângela T. W De Mamann ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Biomass Yield ◽  
Multiple Linear Regression Model ◽  
Thermal Time ◽  
Linear Regression Models ◽  
Split Application ◽  
N Supply

ABSTRACT Wheat biomass yield focused on the production of quality silage is dependent on rainfall, temperature and nitrogen (N). The objective of the study was to validate the use of rainfall, thermal time and N as potential variables for the composition of the multiple linear regression model and simulation of wheat biomass yield for silage production under N supply conditions during the cycle, in the systems of succession. The study was conducted in 2012, 2013 and 2014, in randomized blocks with four replicates in 4 x 3 factorial, for N-fertilizer doses (0, 30, 60, 120 kg ha-1) and forms of N supply [single application (100%) in the stage V3 (third expanded leaf); split application (70%/30%) in the stages V3/V6 (third and sixth expanded leaves); split application (70%/30%) in the stages V3/E (third expanded leaf and beginning of grain filling)], respectively, in the systems soybean/wheat and maize/wheat. Rainfall and N are potential variables in the composition of the multiple linear regression model. Multiple linear regression models are efficient in the simulation of wheat biomass yield for silage under the N supply conditions during the cycle in the succession systems.

scholarly journals Determinants of Knowledge and Attitude towards Breastfeeding in Rural Pregnant Women Using Validated Instruments in Ethiopia

2021 ◽  
Vol 18 (15) ◽  
pp. 7930
Author(s):  
Misra Abdulahi ◽  
Atle Fretheim ◽  
Alemayehu Argaw ◽  
Jeanette H. Magnus
Keyword(s):  
Linear Regression ◽  
Pregnant Women ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Educational Interventions ◽  
Attitude Scale ◽  
Linear Regression Models ◽  
Breastfeeding Knowledge ◽  
Breastfeeding Practices ◽  
Knowledge And Attitude

Understanding the underlying determinants of maternal knowledge and attitude towards breastfeeding guides the development of context-specific interventions to improve breastfeeding practices. This study aimed to assess the level and determinants of breastfeeding knowledge and attitude using validated instruments in pregnant women in rural Ethiopia. In total, 468 pregnant women were interviewed using the Afan Oromo versions of the Breastfeeding Knowledge Questionnaire (BFKQ-AO) and the Iowa Infant Feeding Attitude Scale (IIFAS-AO). We standardized the breastfeeding knowledge and attitude scores and fitted multiple linear regression models to identify the determinants of knowledge and attitude. 52.4% of the women had adequate knowledge, while 60.9% of the women had a neutral attitude towards breastfeeding. In a multiple linear regression model, maternal occupation was the only predictor of the BFKQ-AO score (0.56SD; 95%CI, 1.28, 4.59SD; p = 0.009). Age (0.57SD; 95%CI, 0.24, 0.90SD; p = 0.001), parity (−0.24SD; 95%CI, −0.47, −0.02SD; p = 0.034), antenatal care visits (0.41SD; 95%CI, 0.07, 0.74SD; p = 0.017) and the BFKQ-AO score (0.08SD; 95% CI, 0.06, 0.09SD; p < 0.000) were predictors of the IIFAS-AO score. Nearly half of the respondents had inadequate knowledge and most women had a neutral attitude towards breastfeeding. Policymakers and managers could address these factors when planning educational interventions to improve breastfeeding practices.

Applications of a Multiple Linear Regression Model to the Analysis of Relationships between Eastward- and Westward-Moving Intraseasonal Modes

2004 ◽  
Vol 61 (24) ◽  
pp. 3041-3048 ◽  
Author(s):  
Paul E. Roundy ◽  
William M. Frank
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Spatial Scales ◽  
Statistical Significance ◽  
Multiple Linear Regression Model ◽  
Longwave Radiation ◽  
Linear Regression Models ◽  
Linear Predictor

Abstract Multiple linear regression models with nonlinear power terms may be applied to find relationships between interacting wave modes that may be characterized by different frequencies. Such regression techniques have been explored in other disciplines, but they have not been used in the analysis of atmospheric circulations. In this study, such a model is developed to predict anomalies of westward-moving intraseasonal precipitable water by utilizing the first through fourth powers of a time series of outgoing longwave radiation that is filtered for eastward propagation and for the temporal and spatial scales of the tropical intraseasonal oscillations. An independent and simpler compositing method is applied to show that the results of this multiple linear regression model provide a better description of the actual relationships between eastward- and westward-moving intraseasonal modes than a regression model that includes only the linear predictor. A statistical significance test is applied to the coefficients of the multiple linear regression model, and they are found to be significant over broad regions of the Tropics. Correlations between the predictors are shown to not significantly influence results for this case. Results show that this regression model reveals physical relationships between eastward- and westward-moving intraseasonal modes. The physical interpretation of these regression relationships is given in a companion paper.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PENDAPATAN NELAYAN DI KABUPATEN ACEH SELATAN (STUDI KASUS DI DESA SAWANG BA’U)

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
Yasrizal Yasrizal
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Work Experience ◽  
Sampling Method ◽  
Total Population ◽  
Coastal Region ◽  
Multiple Linear Regression Model ◽  
Sale Price ◽  
Model Sample

Sawang Ba'u is a coastal region village located in South Aceh. There are 1069 fishermen living in this village. This village given  major contribution to the production of fish in South Aceh, which amounted to 3,042 tons/year. Capital, work experience, price, and haul as a measure of the high and low fishermen revenue. Therefore, it is necessary to investigate the effect of capital, work experience, price, and haul to fishermen revenue in the Sawang Ba'u South Aceh. Descriptive quantitative approach is used in this study with multiple linear regression model. Sample of this research are 30 fishermen that are taken from total population using incidental sampling method. Sample shown there are 80% samples earn IDR 51,000 up to IDR 70,000 per day. It is also shown 73% of samples have work experience for 11 to 20 years.  Everyday 45% of samples would be able to catch 6 kg of fish which reach IDR 25,000 up to IDR 35,000. The result of multiple linear regression shows that capital variable, work experience, price and haul simultaniously have effect on fishermen revenue at Sawang Ba’u village, South Aceh District. In conclusion, capital, sale price and haul of fishes effect the fishermen revenue.

scholarly journals Multi-criteria approach to pair-multiple linear regression models constructing

2021 ◽  
Vol 21 (1) ◽  
pp. 88-99
Author(s):  
Mikhail P. Bazilevskiy ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Deming Regression ◽  
Multiple Linear Regression Models

A pair-multiple linear regression model which is a synthesis of Deming regression and multiple linear regression model is considered. It is shown that with a change in the type of minimized distance, the pair-multiple regression model transforms smoothly from the pair model into the multiple linear regression model. In this case, pair-multiple regression models retain the ability to interpret the coefficients and predict the values of the explained variable. An aggregated quality criterion of regression models based on four well-known indicators: the coefficient of determination, Darbin – Watson, the consistency of behaviour and the average relative error of approximation is proposed. Using this criterion, the problem of multi-criteria construction of a pair-multiple linear regression model is formalized as a nonlinear programming problem. An algorithm for its approximate solution is developed. The results of this work can be used to improve the overall qualitative characteristics of multiple linear regression models.

Comparison of Artificial Neural Network Models and Multiple Linear Regression Models in Cargo Port Performance Prediction

2011 ◽  
Vol 403-408 ◽  
pp. 3570-3577 ◽  
Author(s):  
P.Oliver Jayaprakash ◽  
K. Gunasekaran ◽  
S. Muralidharan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Service Time ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Neural Network Models ◽  
Operational Characteristics ◽  
Artificial Neural

Cargo ports operational performance was specified typically through revenue earned, quantum of cargo handled and number of ships serviced. It was predisposed by infrastructural facilities and cargo handling rate; it had an effect over pre-berth waiting time of vessels waiting and berthing time of ships at a port. An Indian port’s ship movement and port operational characteristics had been studied for five years (2005-2009). Ship’s service time was the crucial parameter used to quantify the port performance. This paper focused on building an artificial neural network technique based model to illustrate the relationship between service time and port operational characteristics. Validations of ANN model, comparing multiple linear regression model outputs were reported.

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