scholarly journals PEMODELAN JUMLAH KASUS PNEUMONIA BALITA DI JAWA TIMUR MENGGUNAKAN REGRESI SPATIAL AUTOREGRESSIVE MOVING AVERAGE

2019 ◽  
Vol 8 (3) ◽  
pp. 236
Author(s):  
MADE NARYMURTI WIDYASTUTI ◽  
I GUSTI AYU MADE SRINADI ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Analysis ◽  
Health Services ◽  
Moving Average ◽  
Coefficient Of Determination ◽  
Autoregressive Moving Average ◽  
Spatial Autoregressive ◽  
Significant Factors

The purpose of this study is to model and determine the factors that significantly influence the number of toddler pneumonia cases in East Java Province. Modeling the number of toddler pneumonia cases was conducted using spatial autoregressive moving average (SARMA) regression analysis. The results showed that the best model to modeling was SARMA (1.1) with the AIC value is  and the coefficient of determination ( is . The significant factors that affect the number of these cases are the number of toddler receiving complete basic immunization and the number of toddler receiving health services in each district/city.

scholarly journals PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017

2020 ◽  
Vol 4 (1) ◽  
pp. 164-178
Author(s):  
Hardani Prisma Rizky ◽  
Wara Pramesti ◽  
Gangga Anuraga
Keyword(s):  
Moving Average ◽  
Spatial Regression ◽  
P Value ◽  
Autoregressive Moving Average ◽  
Test Results ◽  
Tuberculosis Patients ◽  
Weighting Matrix ◽  
Regression Approach ◽  
Spatial Dependencies ◽  
Spatial Autoregressive

Tuberculosis (TB) is a contagious infectious disease caused by the bacterium Mycobacterium tuberculosis which can attack various organs, especially the lungs. TB if left untreated or incomplete treatment can cause dangerous complications to death. East Java Province has the second-highest TB case after West Java Province. Therefore we need statistical modeling to analyze the factors that influence TB in East Java Province. The data used in this study were sourced from data from BPS and East Java Provincial Health Offices in 38 districts/cities in East Java Province in 2017. Analysis of data using the OLS regression approach only looked at variable factors but was unable to know the effects of territory. So to overcome this, a spatial regression approach is used by comparing the weight of Queen Contiguity and the results of the k-means cluster analysis to obtain the best model. Based on the results of the analysis, the spatial aspects of the data have met the assumptions of spatial dependencies using the Moran's I test with a p-value of 0.000001295. The weighting matrix used is the k-means cluster weighting matrix k = 2. The test results obtained by the Spatial Autoregressive Moving Average (SARMA) model selected as the best model with the value of the deterrence coefficient (R2) and Akaike Info Criterion (AIC), 87.10% and 586.69. The factors that significantly influence the number of Tuberculosis patients in each district/city in East Java are population density (X2) and the number of healthy houses (X9).

A Study on Fractal Characteristics of ARMA Model

1996 ◽  
Vol 118 (4) ◽  
pp. 677-680 ◽  
Author(s):  
M. Hasegawa ◽  
J. C. Liu ◽  
K. Okuda ◽  
M. Nunobiki
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Moving Average ◽  
Arma Model ◽  
Fractal Dimensions ◽  
Variation Method ◽  
Autoregressive Moving Average ◽  
Fractal Characteristic ◽  
Fractal Characteristics

This paper discusses the fractal characteristics of the autoregressive moving average (ARMA) model, which has been considered as one of the useful approaches for investigating the random engineering phenomena. Firstly, the fractal characteristic of the ARMA model is proven using the variation method. Then, based on this result, the relationships between the fractal dimensions of the AR (1), the AR (2) and the ARMA (2,1) models and autoregressive and moving average parameters of these models are illustrated quantitatively by using the multiple regression analysis.

FRAUD CONFIRMATION, CLIENT SATISFACTION AND CLIENT LOYALTY

2019 ◽  
Vol 1 (1) ◽  
pp. 39
Author(s):  
Ngurah Pandji Mertha Agung Durya
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Client Satisfaction ◽  
Audit Quality ◽  
Hypothesis Test ◽  
Linear Regression Analysis ◽  
Quality Attributes ◽  
Path Model ◽  
Coefficient Of Determination ◽  
The Government

<p>This study aims to find evidence, the influence of Audit Quality Attributes, Client Satisfaction and Client Loyalty, which are moderated by Fraud Confirmation. The research was conducted at the BKM, a community-based organization, formed by the Government, through the <em>Kotaku</em> Program. The research used Regression statistical analysis and conducted a hypothesis test. Regression analysis used includes Simple Linear Regression Analysis, Multiple Regression Analysis, and MRA Regression Analysis, and Path Model Linear Regression Analysis. This study also pays attention to the calculation of the coefficient of determination to give an idea of the ability of the model in explaining the phenomenon of Client Satisfaction and Client Loyalty. The result that both partially and simultaneously, Audit Quality Attributes, Fraud Confirmation affected Client Satisfaction and Loyalty. The research also succeeded in proving that Client Satisfaction mediates the effect of Audit Quality Attributes on Client Loyalty, but failed to provide empirical evidence, that the Fraud Confirmation moderated the effect of Audit Quality Attributes on Client Satisfaction and Loyalty. Contribution to audit practices, where it is important to realize Client Satisfaction through Audit Quality Attributes and Fraud Confirmation, especially in situations where Fraud acts are suspected.</p>

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

Faktor – Faktor Yang Mempengaruhi Minat Berwirausaha Mahasiswa Program Studi Manajemen Fakultas Ekonomi Universitas Flores

Analisis ◽  
2020 ◽  
Vol 19 (1) ◽  
pp. 76-84
Author(s):  
Nasarius Aban ◽  
Gabriel Tanusi
Keyword(s):  
Emotional Intelligence ◽  
Regression Analysis ◽  
Family Environment ◽  
Linear Regression Analysis ◽  
Simple Random Sampling ◽  
Coefficient Of Determination ◽  
Test Results ◽  
Study Program ◽  
Management Study ◽  
The University

This study aims to determine the effect of emotional intelligence, independent attitude and family environment on the interest in entrepreneurship at the University of Flores Management Faculty of Economics. This research is an associative research. The population in this study were students of the Management Study Program of the Faculty of Economics of the University of Flores in the class of 2015-2016 who had passed the entrepreneurship courses of 170 people. Samples taken in this study were 105 respondents, with sampling techniques using simple random sampling. Data collection using questionnaires and interviews, while data analysis was performed using multiple linear regression analysis. The results of multiple regression analysis are Y = 1.060 + 0.594X1 + 0.114X2 + 0.421X3 + e. The coefficient of determination R2 for the variables X1, X2, X3 is 0.675, which means that entrepreneurial interest can be influenced by emotional intelligence, independent attitude and family environment by 67.50% and the remaining 32.50% is influenced by other factors including factors of education, skills, motivation and others. F test results show the value of Fcount> Ftable (28.442> 2.69) with a significant level of 0.000 <0.05 meaning that there is a positive and significant influence between emotional intelligence, independent attitude and family environment together on the entrepreneurial interest of the Faculty of Management Study Program Students The economy. Partial test results (t) show 1) Emotional intelligence factors have a positive and significant effect on entrepreneurial interest 2) Family environment factors have a positive and significant effect on entrepreneurial interest 3) Independent attitude factor has no positive and significant effect on entrepreneurial interest.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

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