Based on multiple linear regression analysis of the macroeconomic factors affecting real estate prices

2011 ◽  
Author(s):  
Xue-Shi You ◽  
Jin-Xin Tian ◽  
Jian Li
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Real Estate ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Macroeconomic Factors ◽  
Factors Affecting ◽  
Real Estate Prices

scholarly journals Factors Affecting Company's Capability in Performing Integrated Reporting: An Empirical Evidence From Indonesian Companies

2018 ◽  
Vol 7 (2) ◽  
pp. 141
Author(s):  
Putu Sukma Kurniawan ◽  
Made Arie Wahyuni
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Institutional Ownership ◽  
Multiple Linear Regression Analysis ◽  
Managerial Ownership ◽  
Contrast Level ◽  
Integrated Reporting ◽  
Factors Affecting

<p>This study examines the factors that affect the company's capability to perform integrated reporting. The analysis used in testing the hypothesis is multiple linear regression analysis. Results show that company’s size has positive and significant connection and stakeholder’s pressure has negative and significant connection with the company’s capability in performing integrated reporting. In contrast, level of company’s profitability, company’s managerial ownership, and company’s institutional ownership did not have enough connection with company’s capability in performing integrated reporting.</p><p> </p>

scholarly journals Production Analysis of Green Mussel (Perna viridis) in Lampung Province

2020 ◽  
Vol 8 (2) ◽  
pp. 975
Author(s):  
Sulvina Sulvina ◽  
Zainal Abidin ◽  
Supono Supono
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Influential Factors ◽  
Perna Viridis ◽  
Factors Affecting ◽  
Cultivation Process ◽  
Quantitative Descriptive

This study was conducted to find out factors affecting and level of mussel production, level of efficiency of using the tools and materials in cultivation process and whether the cultivation of mussels in Pasaran. This study was analyzed using Cobb-Douglass. The study were analyzed in quantitative descriptive, multiple linear regression analysis, and analysis of efficiency. The dependent variable (Y) is the result of production of green mussels cultivation and free variables are the number of bamboos (X 1), the amount of strap (X2), grouper (X3) and labor (X4). Mussel fisherman in Pasaran NPM with Px calculated to obtain the level of efficiency of each factors of production in messels cultivation. Studies show that the most influential factors production are variable bamboo, rope and labor. While the results of analysis the level of efficiency of using tools and materials is not efficient. The number of bamboo and labor should be reduced, because it tends to be a waste and not profitable either technically or economically. The value of the return to scale of 1.22 showed cultivation mussels are on increasing return to scale.

Wavelet and multiple linear regression analysis for identifying factors affecting particulate matter PM2.5 in Mumbai City, India

2019 ◽  
Vol 36 (10) ◽  
pp. 1750-1783 ◽  
Author(s):  
Vivekanand Venkataraman ◽  
Syed Usmanulla ◽  
Appaiah Sonnappa ◽  
Pratiksha Sadashiv ◽  
Suhaib Soofi Mohammed ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Control Measures ◽  
Data Set ◽  
Content Type ◽  
Factors Affecting ◽  
Significant Factors

Purpose The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM2.5 through wavelet and regression analysis. Design/methodology/approach In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM2.5 as a dependent variable. Findings It is found that few pollutants such as NO2, NOx, SO2, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM2.5. The regression model developed has high R2 value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model. Research limitations/implications The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM2.5. Practical implications Control measures such as control charts can be implemented for significant factors. Social implications Rules and regulations can be made more stringent on the factors. Originality/value The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.

scholarly journals Perspectives from Nurse Managers on Informatics Competencies

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Li Yang ◽  
Dan Cui ◽  
Xuemei Zhu ◽  
Qiuli Zhao ◽  
Ningning Xiao ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Nurse Managers ◽  
Descriptive Analysis ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Nursing Informatics ◽  
Nursing Educators ◽  
Factors Affecting

Background and Purpose. Nurse managers are in an excellent position for providing leadership and support within the institutions they serve and are often responsible for accessing information that is vital to the improvement of health facility processes and patients’ outcomes. Therefore, competency in informatics is essential. The purposes of this study are to examine current informatics competency levels of nurse managers and to identify the variables that influence these competencies.Methods. A questionnaire designed to assess demographic information and nursing informatics competency was completed by 68 nurse managers. Multiple linear regression analysis was conducted to analyze the factors influencing informatics competency.Results. Descriptive analysis of the data revealed that informatics competency of these nurse managers was in the moderate range77.65±8.14. Multiple linear regression analysis indicated that level of education, nursing administration experience, and informatics education/training were significant factors affecting competency levels.Conclusion. The factors identified in this study can serve as a reference for nurse managers who were wishing to improve their informatics competency, hospital administrators seeking to provide appropriate training, and nursing educators who were making decisions about nursing informatics curricula. These findings suggest that efforts to enhance the informatics competency of nurse managers have marked potential benefits.

scholarly journals FACTORS AFFECTING MICRO SMALL AND MEDIUM ENTERPRISES (MSMEs) REVENUE OF PEOPLE’S BUSINESS CREDIT (KUR) RECEIVERS IN DONGGALA REGENCY

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Ary Widdodo ◽  
Rustam Abd Rauf ◽  
Lien Damayanti
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Small And Medium Enterprises ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Snowball Sampling ◽  
Factors Affecting ◽  
Medium Enterprises

This research aims to determine the factors affecting micro small and medium enterprises (MSMEs) revenue of people’s business credit (KUR) receivers in Donggala Regency. Factors that allegedly influenced the income of MSMEs of KUR recipients include turnover, length of business, number of manpower and number of KUR.This research was conducted in December 2016 until January 2017 in Donggala Regency. The number of samples used are 43 debtors from Bank Nasional Indonesia (BNI), Bank Rakyat Indonesia (BRI) and Bank Mandiri. The sample is using snowball sampling method, and the data analyzed using Multiple Linear Regression Analysis. Based on the analysis result, it can be concluded that the factors that influence the revenue of MSMEsof KUR receiver are the turnover, the number of labor and the KUR number while the old factor of business does not affect the revenue of MSMEs receiving KUR

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