A Study on the Prediction of Daily Snowmelt Depth using Multiple Linear Regression

In this study, daily snowmelt was predicted using observed meteorological data and multiple regression analysis. Five observation stations (located in Daegwallyeong, Gwangju, Seosan, Mokpo, and Jeonju) were selected to analyze fresh snow depth from 2000 to 2010. The dependent variable used in the multiple regression analysis was daily snowmelt depth, and the independent variables were fresh snow depth, diurnal temperature range, temperature interception, diurnal humidity range, humidity intercept, and solar radiation. Seventy percent of the total observed data was used to develop a multiple regression model and the regression model was verified using the 30% of remaining data. The adjusted R-squared and Root Mean Square Deviation (RMSE) were used to examine the developed regression model. As a result, the adjusted R-squared was higher than 0.769 (except Daegwallyeong); thus the developed model represented well the daily snowmelt depth. Even Jeonju had an adjusted R-squared of 0.869. Also, the RMSE in all of the five stations was lower than 2.5 cm. The lowest value in Seosan was 1.7 cm. From the two types of verification, the developed multiple regression model was judged to be suitable to predict the daily snowmelt depth. However, multicollinearity should be explained, as rapid increases in temperature and sustained high temperature could not be reflected in the model. Therefore, if the limitations were resolved in further research, the model could be used to predict the amount of daily snowmelt depth more reliably.

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Frequency Analysis of Future Maximum Fresh Snow Depth using Multiple Regression Model with Interaction

Korean Society of Hazard Mitigation ◽

10.9798/kosham.2016.16.2.369 ◽

2016 ◽

Vol 16 (2) ◽

pp. 369-376 ◽

Cited By ~ 2

Author(s):

Heeseong Park ◽

Sangman Jeong ◽

Gunhui Chung

Keyword(s):

Regression Model ◽

Multiple Regression ◽

Frequency Analysis ◽

Snow Depth ◽

Multiple Regression Model ◽

Fresh Snow

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Predicting Compressive Strength of Recycled Aggregate Concrete by Multiple Regression Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.546 ◽

2012 ◽

Vol 253-255 ◽

pp. 546-549 ◽

Cited By ~ 1

Author(s):

Yoon Seok Shin ◽

Gwang Hee Kim

Keyword(s):

Compressive Strength ◽

Regression Analysis ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Construction Industry ◽

Multiple Regression Model ◽

Recycled Aggregate Concrete ◽

Recycled Aggregate ◽

Average Error ◽

Aggregate Concrete

Today, efforts are underway in a number of areas to conserve the environment and protect natural resource. In the construction industry, many researchers have studied the development of new concrete using recycled aggregate (RA). This research proposes a multiple regression model (MRM) for predicting the compressive strength of recycled aggregate concrete (RAC). The compressive strength data of 85 specimens of RAC strengths were used for constructing and evaluating the prediction model. The average error rate of the constructed MRM evaluation is 7.18 percent. This result will be useful for predicting the compressive strength of RAC using multiple regression analysis.

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PENGARUH PELATIHAN DAN MOTIVASI TERHADAP KINERJA KARYAWAN PADA KOPILOKA 3.0 PALEMBANG TAHUN 2021

Jurnal Ilmiah Mahasiswa Perbankan Syariah (JIMPA) ◽

10.36908/jimpa.v1i2.28 ◽

2021 ◽

Vol 1 (2) ◽

pp. 145-152

Author(s):

Juni Eliana Prasetya N ◽

Moh. Faizal ◽

Choirunnisak Choirunnisak

Keyword(s):

Regression Analysis ◽

Data Analysis ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Work Motivation ◽

Employee Performance ◽

Independent Variables ◽

Analysis Technique ◽

Level Of Training ◽

Positive Effect

In this study, there are results that contradict the theory which states that the higher the level of training and motivation, the employee's performance should increase. In this study, training had no effect and motivation had no positive effect. This study aims to determine the effect of training and work motivation on employee performance in Kopiloka 3.0. The population in this study were all employees at Kopiloka 3.0, which amounted to 15 people with a sample of 15 people using saturated sampling. The data analysis technique in this study used SPSS 24 software, which consisted of data quality test, classical assumption test, multiple regression analysis, and hypothesis testing. By using multiple regression analysis, the results of the study show that training has no effect on employee performance. Motivation has a significant effect on employee performance. Simultaneously (together) the independent variables of training and motivation have an effect on employee performance.

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Quality Assurance among Childcare Employees in Selangor

WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS ◽

10.37394/23207.2021.18.137 ◽

2021 ◽

Vol 18 ◽

pp. 1498-1511

Author(s):

Yaty Sulaiman ◽

Ng Kel Lie

Keyword(s):

Quality Assurance ◽

Regression Analysis ◽

Quality Management ◽

Total Quality Management ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Employee Commitment ◽

Total Quality ◽

Independent Variables ◽

Employee Competencies

Caregiver or childcare employee is responsible to produce excellent quality in taking care of the children. A good practice of quality assurance among employees is important to develop the future generations. The objective of this research is to identify the factors that influence quality assurance among childcare employees in Selangor. It is to determine the relationship between factors (physical facilities, employee commitment, communication, and employee competencies) and quality assurance. A random sampling technique was used to select respondents from the registered childcare centers in Selangor, Malaysia. The study used quantitative approach through structured questionnaires developed from previous research. Among 450 survey questionnaires distributed, only 379 questionnaires were accepted and used for analysis. Study uses SPSS 22.0 to analyze data. Methodologies such as Pearson Correlation and Multiple Regression Analysis were conducted in analyzing the data. Findings revealed that quality assurance is having a positive relationship with physical facilities, employee commitment, communication, and employee competencies, with the overall results of the multiple regression analysis showed that all independent variables have relationships with the dependent variable at p=0.000. The result of this test also indicated that all independent variables have positive beta coefficient value as physical facilities, employee commitment, communication and employee competencies at 0.372, 0.251, 0.064 and 0.103 respectively. Thus, this study adds to better understanding of the total quality management (TQM) which is the main underlying theory used to develop the research framework. In general, Total Quality Management (TQM) advised on the factors such as physical facilities, employee commitment, employee competencies, and communication that when applied, could get the best result of employees.

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Research on Related Problems of PM2.5 in Air

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.5319 ◽

2014 ◽

Vol 644-650 ◽

pp. 5319-5324

Author(s):

Tian Jiu Leng

Keyword(s):

Regression Analysis ◽

Boundary Conditions ◽

Regression Model ◽

Multiple Regression ◽

Stepwise Regression ◽

Multiple Regression Model ◽

Stepwise Regression Analysis ◽

Spatial Evolution ◽

Analysis Method ◽

Relevant Factors

In this paper, the relevant factors of PM2.5 and the degree of correlation between them were analyzed.The multiple regression model was established using stepwise regression analysis method and the temporal spatial evolution of PM2.5 was obtained by setting the initial and boundary conditions.

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Application of multiple regression analysis to morphometric characters obtained from Serranus cabrilla (linnaeus, 1758) by using stepwise method

Indian Journal of Animal Research ◽

10.18805/ijar.v0iof.9185 ◽

2017 ◽

Author(s):

Levent Sangün ◽

O. Ýnanç Güney

Keyword(s):

Body Weight ◽

Regression Analysis ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

The Body ◽

Stepwise Multiple Regression ◽

Morphometric Characters ◽

Independent Variables ◽

Fisheries Science ◽

Stepwise Method

In fisheries science, high number of morphometric measures (independent variables) taken from different parts of the fish complicates the estimation of the body weight (dependent variable). Therefore, the researchers are seeking for a solution facilitating the interpretation of the equations of correlation between the characteristics. One way to deal with this challenge is the dimension reduction by means of stepwise multiple regression analysis. The aim of this study is to explain total variation with the same accuracy by using fewer independent variables. To accomplish this, 12 morphometric measures from 210 individuals of Serranus cabrilla were measured to estimate the body weight. Firstly, the 95% of the variation was explained by means of multiple regression analysis by using all variables. Then, by step-wise method, the same results were achieved with fewer independent variables. Finally, the variables with inter-multicollinearity eliminated and with two remaining independent variables determination coefficients resulted as 95%. The result showed that using more variables does not create significant distinction for accuracy to estimate the body weight although; the total length and body dept was the most effective features for weight.

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P-431 Updated multiple regression analysis of prognostic variables for survival in malignant pleural mesothelioma (MPM): Observations from a phase III study of pemetrexed plus cisplatin versus cisplatin and evolution of the regression model

Lung Cancer ◽

10.1016/s0169-5002(05)80924-9 ◽

2005 ◽

Vol 49 ◽

pp. S229 ◽

Cited By ~ 2

Author(s):

J. Symanowski ◽

J. Rusthoven ◽

N. Vogelzang

Keyword(s):

Regression Analysis ◽

Regression Model ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Malignant Pleural Mesothelioma ◽

Phase Iii ◽

Pleural Mesothelioma ◽

Prognostic Variables ◽

Phase Iii Study

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Evaluation of Motor Transport Infrastructure Influence on Socio-Economic Indicators of Minsk Region

Science & Technique ◽

10.21122/2227-1031-2018-17-4-314-319 ◽

2018 ◽

Vol 17 (4) ◽

pp. 314-319

Author(s):

R. B. Ivut ◽

P. V. Popov ◽

P. I. Lapkovskaya ◽

I. V. Emelyanovich

Keyword(s):

Economic Development ◽

Regression Analysis ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Transport Infrastructure ◽

Economic Indicators ◽

Infrastructure Development ◽

Independent Variables ◽

Socio Economic Development ◽

The Republic

The paper substantiates the necessity to study a transport infrastructure in the context of its impact on socioeconomic indicators pertaining to the development of a certain region. The importance of this study is confirmed by the goals and objectives of the transport system development of the Republic of Belarus, which are outlined in the National Strategy of the Republic of Belarus for Sustainable Socio-Economic Development for the period until 2030, and they are considered as well as priority areas for the development of Minsk region, presented in the Sustainable Development Strategy of Minsk Region for 2016–2025. The influence of eight indicators for transport infrastructure development of Minsk region on four key socio-economic indicators of the region development such as gross regional product, investment in fixed assets, revenues of consolidated budgets, average wage level has been investigated in the paper. A multiple regression analysis has been applied for assessing a relationship between dependent and independent variables. A step selection method has been used as a method of multiple regression analysis. All calculations have been carried out in the IBM SPSS Statistics 20 program. As a result of the study correlation indicators have been established between dependent and independent variables, relationships between variables and dependencies have been determined in the paper. The reliability of the obtained results have been verified with the help of the Fisher and Student's criteria. The obtained results have made it possible to identify key indicators for a transport infrastructure of Minsk region which have the greatest impact on socio-economic development of this region and can be used to analyze an investment attractiveness of Minsk region.

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Mencari Variabel-variabel Yang Mempengaruhi Sikap Mental Entrepreneural (Wirausaha)Pada Pemerintah Kabupaten Serdang Bedagai

Media Riset Akuntansi Auditing & Informasi ◽

10.25105/mraai.v6i2.916 ◽

2006 ◽

Vol 6 (2) ◽

pp. 155

Author(s):

Arfan Ikhsan

Keyword(s):

Regression Analysis ◽

Key Words ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Mental Attitude ◽

Independent Variables ◽

Independent Variable

This research try to find some variables that influence entrepreneurial mental attitude at government of Serdang Bedagai Regency. independent variable which considered in this research is hierarchy, formalizes, trust, mission, ethics and regulation barriers level. Those independent variables will be able to improve society satisfaction, reduction of level of bureaucracy and improvement of risking tendency. The data of this research collected from organization chart of Serdang Bedagai Regency. Amount of 68 responders participated in this research. Hereinafter this data processed by using multiple regression analysis. The result shows that formalizes, trust, mission, ethics and regulation barriers level have an effect significantly toward society satisfaction and also bureaucracy. Meanwhile risking tendency is significantly influenced by hierarchy, formalizes, mission and ethics.Key Words: hierarchy, formalizes, trust, mission, ethics and regulation barriers level

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PENGARUH PAJAK, SUBSIDI DAN ZIS TERHADAP PENURUNAN KEMISKINAN DI INDONESIA

Media Ekonomi ◽

10.25105/me.v22i2.3170 ◽

2013 ◽

Vol 22 (2) ◽

pp. 123

Author(s):

Hikmathiar Ramadhan ◽

Tatik Mariyanti

Keyword(s):

Regression Analysis ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Regression Method ◽

The State ◽

Independent Variables ◽

Multiple Regression Method ◽

The Relationship

This paper discusses the analysis of the effect of taxes and subsidies and ZIS (Zsakat, Infaq and Sedekah) to the reduction of poverty in Indonesia in the period 1994 to 2013. The variables used in include taxes, subsidies, ZIS and poverty of the State of Indonesia. The method used in this thesis is a multiple regression method along with the classical assumptions. This study aims to determine the relationship between variables taxes, subsidies and ZIS against poverty. In addition, this study also aims to look at the variables which play a role in reducing poverty. The results using multiple regression analysis, it appears that all the independent variables are not significant and have adjusted R-square is nice. Next to the classical assumption test, based on the results of research there is a violation of classical assumptions in this research is the Normality.

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