Construction of new financial e-commerce model for small and medium-sized enterprises financing based on multiple linear logistic regression

This paper expounds on the development prospects of SMEs and E-commerce finance, and illustrates the significance of developing online finance. It also introduces the commonly-used research methods of the two kinds of financial models, such as multiple linear regression method and logistic regression method, and analyzes the reasons for the financing difficulties of SMEs. Currently, the high financing cost is the main reason for the financing difficulties of SMEs. Several reasons are account for the high financing cost. Among them, high financing cost,low-efficiency financial system,long financing cycle and the loan information asymmetry account for 35%, 21%, 19% and25% respectively. In addition, this paper clarifies the advantages and disadvantages of network finance and the necessity of developing online finance.

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ANALISIS PENGARUH AGENCY COST TERHADAP DIVIDEND PAYOUT RATIO (Pada Perusahaan Manufaktur di Bursa Efek Indonesia Tahun 2010-2011)

Jurnal Kajian Akuntansi dan Auditing ◽

10.37301/jkaa.v0i0.6498 ◽

2013 ◽

Author(s):

Raudhatul Hidayah

Keyword(s):

Firm Size ◽

Sampling Method ◽

Dividend Policy ◽

Stock Exchange ◽

Institutional Ownership ◽

Agency Cost ◽

Regression Method ◽

Linear Regression Method ◽

Dividend Payout ◽

Multiple Linear Regression Method

The main purpose of the research was to know partially the influence of institutional ownership, collateralizable assets, debt to total assets and firm size on dividend payout ratio in firms that listed at Indonesia Stock Exchange of 2010–2011 period. The other purpose is to know simultaneously the influence of institutional ownership, collateralizable assets, debt to total assets and firm size on dividend payout ratio in firms that listed at Indonesia Stock Exchange of 2010–2011 period. The population of this research was all the firms that listed at Indonesia Stock Exchange of 2010-2011 period namely, 136 in number. The sample, 27 firms, was taken by the use of purposive sampling method. The technique of data collection used was documentation. The data analysis made use of multiple linear regression method. The results showed that partially institutional ownership had a positive and significant effect to dividend policy. Collateralizable assets, debt to total assets and firm size partially was not significant to dividend policy. Simultaneously institutional ownership, collateralizable assets, debt to total assets and firm size had a positive and significant effect to dividend payout ratio.

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ANALISIS PENGARUH AGENCY COST TERHADAP DIVIDEND PAYOUT RATIO (Pada Perusahaan Manufaktur di Bursa Efek Indonesia Tabun 2010-2011)

Jurnal Kajian Akuntansi dan Auditing ◽

10.37301/jkaa.v0i0.5380 ◽

2013 ◽

Author(s):

Raudhatul Hidayah

Keyword(s):

Firm Size ◽

Sampling Method ◽

Dividend Policy ◽

Stock Exchange ◽

Institutional Ownership ◽

Agency Cost ◽

Regression Method ◽

Linear Regression Method ◽

Dividend Payout ◽

Multiple Linear Regression Method

The main purpose of the research was to know partially the influence of institutional ownership, collateralizable assets, debt to total assets and firm size on dividend payout ratio in firms that listed at Indonesia Stock Exchange of 2010-2011 period. The other purpose is to know simultaneously the influence of institutional ownership, collateralizable assets, debt to total assets and firm size on dividend payout ratio in firms that listed at Indonesia Stock Exchange of 2010-2011 period. The population of this research was all the firms that listed at Indonesia Stock Exchange of 2010-2011 period namely, 136 in number. The sample, 27 firms, was taken by the use of purposive sampling method. The technique of data collection used was documentation. The data analysis made use of multiple linear regression method. The results showed that partially institutional ownership had a positive and significant effect to dividend policy. Collateralizable assets, debt to total assets and firm size partially was not significant to dividend policy. Simultaneously institutional ownership, collateralizable assets, debt to total assets and firm size had a positive and significant effect to dividend payout ratio.

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Modelling the venture capital investments via logistic regression method

10.1063/5.0026494 ◽

2020 ◽

Author(s):

Güneş Aygüneş

Keyword(s):

Logistic Regression ◽

Venture Capital ◽

Regression Method ◽

Capital Investments ◽

Logistic Regression Method ◽

Venture Capital Investments

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

Journal of Pharmaceutical and Biomedical Analysis ◽

10.1016/j.jpba.2018.11.024 ◽

2019 ◽

Vol 164 ◽

pp. 681-689 ◽

Cited By ~ 4

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

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Improvement of multiple linear regression method for statistical downscaling of monthly precipitation

International Journal of Environmental Science and Technology ◽

10.1007/s13762-017-1511-z ◽

2017 ◽

Vol 15 (9) ◽

pp. 1897-1912 ◽

Cited By ~ 4

Author(s):

H. A. Pahlavan ◽

B. Zahraie ◽

M. Nasseri ◽

A. Mahdipour Varnousfaderani

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Statistical Downscaling ◽

Regression Method ◽

Linear Regression Method ◽

Monthly Precipitation ◽

Multiple Linear Regression Method

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PENGARUH DISKRIMINASI DAN PEMERIKSAAN PAJAK TERHADAP PERSEPSI WAJIB PAJAK ORANG PRIBADI MENGENAI PENGGELAPAN PAJAK DI KOTA BITUNG (Studi Kasus Pada WPOP yang ditemui di KPP Pratama Bitung)

GOING CONCERN : JURNAL RISET AKUNTANSI ◽

10.32400/gc.12.2.17961.2017 ◽

2017 ◽

Vol 12 (2) ◽

Author(s):

Prielly Natasya Kartini Widjaja ◽

Linda Lambey ◽

Stanley Kho Walandouw

Keyword(s):

Tax Evasion ◽

Sampling Method ◽

Regression Method ◽

Linear Regression Method ◽

Multiple Linear Regression Method ◽

Tax Laws ◽

Tax Audit ◽

Convenience Sampling ◽

Spss Software ◽

Tax Payers

Tax is the highest revenue of the country that. Therefore, government continues to increase the source of revenue derived from taxes. However, in reality there are many taxpayers who do not obey tax laws by evading their taxes. Tax Evasion is a taxpayer's act who always tries to minimize the tax payable by violating the provisions of tax laws. The purpose of this research is to find out the effect of tax discrimination and tax audit of tax evasion. Data were collected by questionnaires. Questionnaires were filled up by 100 tax payers in KPP Pratama Bitung. Technique sampling is non probability sampling with convenience sampling or accidental sampling method. This research used multiple linear regression method and SPSS software version 21.0 was utilized. The result shows tax discrimination has no effect on tax evasion and tax audit influence on tax evasion.Keywords: discrimination, tax audit, tax evasion.

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Household Financial Situation during the COVID-19 Pandemic with Particular Emphasis on Savings—An Evidence from Poland Compared to Other CEE States

Risks ◽

10.3390/risks9090166 ◽

2021 ◽

Vol 9 (9) ◽

pp. 166

Author(s):

Grażyna Szustak ◽

Witold Gradoń ◽

Łukasz Szewczyk

Keyword(s):

Well Being ◽

Regression Method ◽

Linear Regression Method ◽

Household Savings ◽

Main Aspect ◽

Financial Decisions ◽

Econometric Methods ◽

Multiple Linear Regression Method ◽

The Impact ◽

Research Show

The aim of this article is to analyze and assess the impact of the pandemic on the finances of households in Poland, compared to other CEE countries (including Czech Republic, Slovakia and Hungary), with particular emphasis on changes in the level of their savings, which are considered to be the foundation for the development of the indicated research group. There is no doubt that the pandemic had an impact on the situation of households, which is mainly visible in the labor market (rising unemployment), and thus the question arises to what extent have the households’ approaches to financial decisions changed because of this situation? The propensity to save was taken into account as a main aspect of this problem, because it has, among others, a big impact on the financial well-being (in a broader sense). Using the multiple linear regression method, the factors that influence the level of household savings were determined. The results of the research show that these factors are different in the analyzed countries and have a different impact on the level of the explained variable, which is the gross saving rate. The research should also be treated as a preliminary one. It constitutes a contribution to in-depth research with the use of more sophisticated statistical and econometric methods, which will allow for the better assessment of the examined issue.

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Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang

AITI ◽

10.24246/aiti.v17i1.42-55 ◽

2020 ◽

Vol 17 (1) ◽

pp. 42-55

Author(s):

Radius Tanone ◽

Arnold B Emmanuel

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Regression Method ◽

Learning Approach ◽

Know How ◽

Independent Variables ◽

Machine Learning Approach ◽

Logistic Regression Method ◽

Python Programming

Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.

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Talent Performance Analysis Using People Analytics Approach

Budapest International Research and Critics Institute (BIRCI-Journal) Humanities and Social Sciences ◽

10.33258/birci.v4i1.1585 ◽

2021 ◽

Vol 4 (1) ◽

pp. 216-230

Author(s):

Fahreza Nasril ◽

Dian Indiyati ◽

Gadang Ramantoko

Keyword(s):

Logistic Regression ◽

Multinomial Logistic Regression ◽

Research Question ◽

Development Program ◽

Individual Performance ◽

Study Data ◽

Regression Method ◽

Process Data ◽

Logistic Regression Method ◽

The Right

The purpose of this study was to answer the research question "How is the prediction of Talent Performance in the following year with the application of People Analytics?" and knowing the description of employees who are potential talents, the resulting performance contributions, to the description of the development and retention efforts needed by Talent in order to be able to maintain their future performance and position as Talents compared to the previous People Analytics method using predictive analysis, namely prediction of Talent Performance in the year next. In this study, data analysis using the Multivariate Logistic Regression method is used to get the Prediction of the Performance of Talents who become the object of research in the form of individual performance quickly and precisely in accordance with the patterns drawn by individual Performance score data in previous years. And can provide insight regarding the projected strategies that need to be done to maintain the improvement of individual talent performance in the years of the assessment period. It also helps management in making decisions about the right Talent development program and determining which Talents are priorities. The population in this study were the talents of employees of PT. Angkasa Pura II (Persero) with a managerial level consisting of: Senior Leader, Middle Leader, and First Line Leader who has a Person Grade (PG) range of 13 to 21. The sample used is Middle Leader level talent with specified criteria and through a process data cleansing. The results of this study indicate that the variable that significantly affects the performance of the following year is the performance of the previous 2 years. Then prediction analysis can be done using these independent variables with the Multinomial Logistic Regression method, and to get prediction results with better accuracy can be done by the Random Forest method.

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