Mixture Markov regression model with application to mosquito surveillance data analysis

2017 ◽  
Vol 59 (3) ◽  
pp. 462-477 ◽  
Author(s):  
Xin Gao ◽  
Yurong R. Cao ◽  
Nicholas Ogden ◽  
Louise Aubin ◽  
Huaiping P. Zhu
Keyword(s):  
Data Analysis ◽  
Regression Model ◽  
Surveillance Data ◽  
Mosquito Surveillance

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals Surveillance data analysis on Measles cases, Anambra State, Nigeria, 2011-2016

2018 ◽  
Vol 73 ◽  
pp. 266-267
Author(s):  
C. Obagha ◽  
S. Gidado ◽  
B. Uba ◽  
S. Ajisegiri ◽  
P. Nguku ◽  
...  
Keyword(s):  
Data Analysis ◽  
Surveillance Data ◽  
Anambra State

PENGARUH BUYING IMPULSIVENESS TERHADAP E-IMPULSIVE BUYING BEHAVIOR DENGAN HEDONIC VALUE DAN UTILITARIAN VALUE SEBAGAI VARIABEL PEMEDIASI

2019 ◽  
Vol 14 (1) ◽  
pp. 61
Author(s):  
Melani Dwi Ratnasari
Keyword(s):  
Data Analysis ◽  
Regression Model ◽  
T Test ◽  
Buying Behavior ◽  
Hedonic Value ◽  
Utilitarian Value ◽  
Impulsive Buying ◽  
The Relationship

The purpose of this study was to examine the hedonic value mediation and utilitarian value variables on the relationship between buying impulsiveness and e-impulsive buying behavior. In this study the number of samples was 123 students using Shopee at Sarjanawiyata Tamansiswa University. Data analysis using a regression model with t test and sobel test. The results showed, 1) impulsiveness buying does not affect hedonic value 2) impulsiveness buying does not affect utilitarian value 3) hedonic value influences e-impulsive buying behavior 4) utilitarian value influences e-impulsive buying behavior 5) impulsiveness buying affects e-impulsive buying behavior 6) hedonic value is not supported as the mediating variable of the relationship between buying impulsiveness and e-impulsive buying behavior 7) supported utilitarian value as the mediating variable of the relationship between buying impulsiveness and e-impulsive buying behavior. Keywords: Shopee, Buying Impulsiveness, E-impulsive Buying Behavior, Hedonic Value, Utilitarian Value ABSTRAK  Tujuan penelitian ini adalah untuk menguji variabel mediasi hedonic value dan utilitarian value pada hubungan antara  buying impulsiveness dan e-impulsive buying behavior. Dalam penelitian ini jumlah sampel adalah 123 orang mahasiswa pengguna Shopee di Universitas Sarjanawiyata Tamansiswa. Analisis data menggunakan model regresi dengan uji t dan sobel test. Hasil penelitian menunjukkan, 1) buying impulsiveness tidak berpengaruh terhadap hedonic value 2) buying impulsiveness tidak berpengaruh terhadap utilitarian value 3) hedonic value berpengaruh terhadap e-impulsive buying behavior 4) utilitarian value berpengaruh terhadap e-impulsive buying behavior 5) buying impulsiveness berpengaruh terhadap e-impulsive buying behavior 6) hedonic value tidak terdukung sebagai variabel pemediasi hubungan antara buying impulsiveness dengan e-impulsive buying behavior 7) utilitarian value terdukung sebagai variabel pemediasi hubungan antara buying impulsiveness dengan e-impulsive buying behavior. Kata-kata Kunci: Shopee, Pembelian tidak terduga, pembelian tidak terencana, nilai kesenangan, nilai manfaat

scholarly journals Epidemiology of HIV Infection in the Amhara Region of Ethiopia, 2015 to 2018 Surveillance Data Analysis

2020 ◽  
Vol Volume 12 ◽  
pp. 307-314
Author(s):  
Etsehiwot Debe Worku ◽  
Mulusew Andualem Asemahagn ◽  
Melese Linger Endalifer
Keyword(s):  
Data Analysis ◽  
Hiv Infection ◽  
Surveillance Data ◽  
Amhara Region

National measles surveillance data analysis, 2005 to 2009, Ethiopia

2016 ◽  
Vol 8 (3) ◽  
pp. 27-37 ◽  
Author(s):  
Bezabih Beyene Belay ◽  
G libanos G Selassie Ghidey ◽  
Ademe Tegegne Aysheshim ◽  
Jima Wayessa Daddi ◽  
Enqueselassie Fikre
Keyword(s):  
Data Analysis ◽  
Surveillance Data

Haar Wavelets in Data Analysis

2010 ◽  
Vol 121-122 ◽  
pp. 346-349
Author(s):  
Yu Qin Sun ◽  
Yuan Ttao Jiang ◽  
Yong Ge Tian
Keyword(s):  
Data Analysis ◽  
Regression Model ◽  
Approximation Theory ◽  
Regression Models ◽  
Haar Wavelets ◽  
General Regression

One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.

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