Structure–property relationships of aliphatic esters using topological descriptors and backward ‐ multiple linear regression method

2020 ◽  
Vol 67 (8) ◽  
pp. 1338-1347
Author(s):  
Mehdi Rajabi ◽  
Fatemeh Shafiei
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Topological Descriptors ◽  
Linear Regression Method ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Multiple Linear Regression Method ◽  
Aliphatic Esters

An application of QSRR approach and multiple linear regression method for lipophilicity assessment of flavonoids

2019 ◽  
Vol 164 ◽  
pp. 681-689 ◽  
Author(s):  
Mariusz Zapadka ◽  
Mateusz Kaczmarek ◽  
Bogumiła Kupcewicz ◽  
Przemysław Dekowski ◽  
Agata Walkowiak ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Multiple Linear Regression Method

scholarly journals Accident Prediction Model at Unsignalized Intersection Using Multiple Linear Regression Method

2017 ◽  
Vol 1 (2) ◽  
pp. 379-389
Author(s):  
N. K. Oghoyafedo ◽  
J. O. Ehiorobo ◽  
Ebuka Nwankwo
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Design Standards ◽  
Accident Prediction ◽  
Geometric Characteristics ◽  
Unsignalized Intersections ◽  
Multiple Linear Regression Method

The issue of road accidents is an increasing problem in developing countries. This could be due to increasing road traffic/vehicle occupancy, geometric characteristics and road way condition. The factors influencing accidents occurrence are to be analysed for remedies. The purpose of this research is to develop an accident prediction model as a measure for future study, aid planning phase preceding the designed intervention, enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections. Five intersections were selected randomly within Benin City and traffic count carried out at these intersections as well as geometric characteristics and roadway conditions. The prediction model was developed using multiple linear regression method and the standard error of estimate was computed to show how close the observed value is to the regression line. The model was validated using coefficient of multiple determination. The establishment of the relationship between accidents and traffic flow site characteristics on the other hand would enable improvement to be more realistically accessed. This study will also enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections.

scholarly journals THE EFFECT OF LEADERSHIP, JOB SATISFACTION AND ORGANIZATIONAL COMMITMENT ON EMPLOYEE PERFORMANCE AT RUMAH SAKIT UMUM DAERAH BESUKI SITUBONDO, INDONESIA

2018 ◽  
Vol 6 (6) ◽  
pp. 322-334
Author(s):  
Amrozi ◽  
Zarah Puspitaningtyas ◽  
Djoko Poernomo
Keyword(s):  
Job Satisfaction ◽  
Organizational Commitment ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Random Sampling ◽  
Employee Performance ◽  
Sampling Technique ◽  
Regression Method ◽  
Linear Regression Method ◽  
Multiple Linear Regression Method

This study is to examine the influence of leadership, job satisfaction and organizational commitment on employee performance. Population in this research was the entire employees of Rumah Sakit Umum Daerah (RSUD) Besuki, Situbondo, Indonesia which was about 295 peoples. Then, the researcher applied probability random sampling technique to select 170 respondents as the sampling. The researcher analyzed the data by applying multiple linear regression method. The result shows that leadership and job satisfaction contribute positive and significant effect on employee performance, while organizational commitment has no effect on employee performance.

scholarly journals Pengaruh Disiplin Kerja dan Pemberian Insentif terhadap Kinerja Karyawan PT Putra Karisma Palembang

2021 ◽  
Vol 2 (1) ◽  
pp. 25-42
Author(s):  
Andini Dwi Saputri ◽  
Susi Handayani ◽  
Muhammad Kurniawan DP
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Employee Performance ◽  
Regression Method ◽  
Linear Regression Method ◽  
Multiple Linear Regression Method ◽  
Sample Technique ◽  
Saturated Sample ◽  
Analyze Data

This study aims to analyze the effects of work discipline and incentives on the performance of employees of PT Putra Karisma Palembang. The sample was selected using the saturated sample technique. Data from 57 respondents were collected through interviews, documentation, and questionnaires. This study implemented the multiple linear regression method to analyze data. The results prove that partially each of work discipline and incentives has no significant effect on employee performance. However, simultaneously work discipline and incentives have a significant effect on employee performance. This insight is beneficial for PT Putra Karisma Palembang. To improve employees' performance, the company should consider both work discipline and incentives factors together.   Penelitian ini bertujuan untuk menganalisis pengaruh disiplin kerja dan pemberian insentif terhadap kinerja karyawan PT Putra Karisma Palembang. Sampel dipilih menggunakan teknik sampel jenuh. Data 57 responden dikumpulkan melalui wawancara, dokumentasi, dan kuesioner. Studi ini mengimplementasikan metode regresi linier berganda untuk menganalisis data. Hasil investigasi membuktikan bahwa secara parsial baik disiplin kerja maupun pemberian insentif masing-masing tidak berpengaruh signifikan terhadap kinerja karyawan. Tetapi, secara simultan disiplin kerja dan pemberian insentif berpengaruh signifikan terhadap kinerja karyawan. Fakta ini bermanfaat bagi PT Putra Karisma Palembang. Untuk meningkatkan kinerja karyawan, maka perusahaan harus mempertimbangkan faktor disiplin kerja dan pemberian insentif secara bersama-sama.

scholarly journals ANALYSIS OF FACTORS AFFECTING THE PRICE FLUCTUATION OF CAYENNE PEPPER IN MALANG REGENCY

2021 ◽  
Vol 5 (1) ◽  
pp. 19-26
Author(s):  
Nurul Laili ◽  
Sri Hindarti ◽  
Dwi Susilowati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Research Method ◽  
Commodity Prices ◽  
Secondary Data ◽  
Regression Method ◽  
Linear Regression Method ◽  
Factors Affecting ◽  
Independent Variables ◽  
Multiple Linear Regression Method

 This study aims to 1) Analyze the pattern of changes in commodity prices for spanish pepper in Malang District. 2) Analyzing the factors that influence fluctuations in the price of spanish pepper in Malang District. The research method used is quantitative method that uses secondary data in the form of time series obtained from several related agencies, namely the Central Statistics Agency of Malang District, Department of Industry and Trade, and Department of food crops, horticulture, and plantation in Malang District. Analysis of the data used is multiple linear regression with the dependent variable is the price at the consumer level from 2009-2018, while the independent variables use the data of the price of spanish pepper at the producer level, the amount of production, and the amount of consumption from 2009-2018. The study found that: 1) The development of the price of spanish pepper had a trend that tended to increase during the last 10 years. 2) From the results of data processing using multiple linear regression method with Eviews 9.0 application, it is found that the factor that significantly influences changes in the price of spanish pepper is the price at the producer level, while the amount of production of spanish pepper and the number of requests does not significantly affect the change in spanish pepper prices in Malang District. 

QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR)

2013 ◽  
Vol 23 (5) ◽  
pp. 2264-2276 ◽  
Author(s):  
Eslam Pourbasheer ◽  
Reza Aalizadeh ◽  
Mohammad Reza Ganjali ◽  
Parviz Norouzi ◽  
Javad Shadmanesh ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Qsar Study ◽  
Multiple Linear Regression Method

scholarly journals PENGARUH MANAJEMEN LABA DAN PERBEDAAN PEMBUKUAN MENURUT PAJAK DAN AKUNTANSI TERHADAP PERINGKAT OBLIGASI

2017 ◽  
Vol 19 (2) ◽  
pp. 260
Author(s):  
Cerelia Chandra ◽  
Hanna Hanna
Keyword(s):  
Linear Regression ◽  
Earnings Management ◽  
Multiple Linear Regression ◽  
Stock Exchange ◽  
Regression Method ◽  
Linear Regression Method ◽  
Observation Data ◽  
Bond Ratings ◽  
Purposive Sampling ◽  
Multiple Linear Regression Method

Penelitian ini bertujuan untuk menguji apakah manajemen laba dan perbedaan pembukuan menurut pajak dan akuntansi berpengaruh terhadap peringkat obligasi. Pemilihan sampel dalam penelitian ini menggunakan metode purposive samplingdari perusahaan-perusahaan yang go public di BEIdan menerbitkan obligasi secara berturut-turut dari bulan Januari 2010 sampai Juni 2013. Perusahaan yang terpilih menjadi sampel penelitian ini berjumlah 13 perusahaan atau sebanyak 182 data observasi. Dalam penelitian ini digunakan metode regresi linier berganda.Hasil penelitian ini menunjukkan bahwa manajemen laba tidak memiliki pengaruh yang signifikan terhadap peringkat obligasi. Perbedaan pembukuan menurut pajak dan akuntansi (book-tax difference) berpengaruh positif dan signifikan terhadap peringkat obligasi.This study aimed to test the effect of earnings management and book-tax differences to the bond ratings. The sample in this study are gathered by using purposive sampling from all companies that listed on the Indonesian Stock Exchange and issued bonds in a row from January 2010 to June 2013. The Company elected as sample amounted to 13 companies or as many as 182 of observation data. In this study, we used multiple linear regression method. The results showed that earnings management does not have a significant effect on bond ratings. While book-tax difference has a positive and significant effect on bond ratings.

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