Classical Multiple Linear Regression Research Articles

Estimating Dam Reservoir Level Change of Istanbul Alibey Dam with The Fuzzy SMRGT Method

Accurate estimation of the dam reservoir level in a dam reservoir is very important for the planning and operation of water structures. In this study, monthly dam reservoir level data between 1989 and 2020 obtained from the State Hydraulic Works (DSI) was used to estimate the monthly dam reservoir level change. For the monthly dam reservoir level estimation, it has been tried to be estimated using the Simple Membership Functionsand Fuzzy Rules Generation Technique (Fuzzy-SMRGT), Artificial Neural Network (ANN) and the classical Multiple Linear Regression (MLR) methods. Alibey Dam located in Sultangazi district of Istanbul was chosen as the study area. The monthly evaporation, water entering into the lake, consumption of drinking water and amount of water discharged from the dam amounts were used to estimate the monthly Alibey Dam reservoir average dam reservoir level. The model results were compared with the actual observation dam reservoir levels. When the results were evaluated, it was observed that the models were successful in estimating the dam reservoir level and gave results close to each other

Factors Affecting the ICI during Indonesia's Economic Recession During the Covid-19 Pandemic

The study used in this study aims to determine the factors that can affect the JCI, namely the rupiah exchange rate, inflation, and interest rates during the Indonesian economic recession during the Covid-19 pandemic. The research method used in this study uses quantitative and The secondary data used is secondary data from the BEI and BI which was collected from 6 Nov 2020 to 3 Feb 2021 with a data collection technique, namely the documentation method. Saturated sampling is used in the sampling technique. So that obtained as many as 59 samples. While the techniques used in data analysis in this study are classical assumption test and multiple linear regression analysis as hypothesis testing. JCI. However, inflation in a partial test does not have a significant effect on the JCI.

Factors Affecting Dissolved Oxygen at Bengawan Solo River: A Spatial Filtering with Eigenvector Technique

The quality of the river changes according to the development of the surrounding environment which is influenced by various human activities. Analysis of factors affecting Dissolved Oxygen (DO) at Bengawan Solo River is crucial for river management purpose and pollution control. Previous research suggested the use classic multiple linear regression. However, DO measurement were usually took place of sampling sites along the river channel. Therefore, there is a high chance that the measurements results may spatially correlated. As the consequence, the utilization of multiple linear regression technique for the dataset can be inappropriate. In this paper, we applied a modification of multiple linear regression model to incorporate with spatial autocorrelation that exist in the data by adding control variable such vector eigen to the model which known as Spatial Filtering with Eigenvector (SFE). The results showed that nitrate and nitrite were the predictor variables that have a negative and significant effect. However, the model contains spatial autocorrelation. The application of SFE technique by adding three eigenvectors as control variables in the model succeeded in making the residual model free from spatial autocorrelation. However, a new problem arose where there was a violation of the non-heteroscedasticity assumption.

Модели машинного обучения в задаче прогнозирования природно-ресурсного потенциала Пермского края

In the article we consider a complex indicator of region natural resource potential modeling and forecasting quality improvement using different machine learning models. Problem under consideration importance is determined by the fact that the models traditionally used for these purposes demonstrate either low quality, or high configuration and parameters evaluation difficulty. The aim of the study is determination of machine learning models that provide the optimal values of various modeling quality metrics. Materials and methods. For this study purposes we considered the multiple linear regression, decision tree, random forest, gradient boosting and multilayer perceptron mo¬dels. We used the determination coefficient R2, the root mean square error of modeling RMSE, the average absolute error of modeling MAE, and the relative error of prediction for 1 and 2 time intervals as quality metrics. This study is based on data of the complex indicator of the Perm Region natural resource potential and the system of its determining factors in the time interval from 2001 to 2018. We evaluate models and calculate quality metrics using Pandas and Scikit-learn Python libraries in Jupiter Notebook environment. Results. According to our research the classical multiple linear regression model demonstrates the worst results for all quality metrics under consideration. The decision tree model demonstrates determination coefficient maximum value and minimum root mean square error and mean absolute error. Minimum relative forecasting error for 2017 is provided by the gradient boosting model, for 2018 – by the multilayer perceptron model. Conclusion. Our study allows us to affirm that nonlinear machine learning models for the task of region natural resource potential modeling and forecasting demonstrate better approximating and predictive properties compared to multiple linear regression and thus can be used to improve the quality of natural resource management.

Efek Harga, Kualitas Pelayanan, dan Kepuasan Pelanggan terhadap Loyalitas Pelanggan PT. Prodia Widyahusada cabang Batam

This study aims to examine and analyze the effect of price, service quality, customer satisfaction on customer loyalty at PT. Prodia Widyahusada Batam Branch. Data collection was carried out by distributing questionnaires to 96 customers of PT. Prodia Widyahusada Batam Branch. The sampling technique used is the questionnaire method and the reliability test data testing technique with Cronbach's Alpha. Classical assumption test and multiple linear regression analysis, to test and prove the research hypothesis. The results of the analysis show that price, service quality and customer satisfaction have a positive and significant effect on customer loyalty. Hypothesis testing using the t test shows that the variables of price, service quality and customer satisfaction that have been studied have a significant effect on customer loyalty variables. Then through the F test, it can be seen that the variables of price, service quality and customer satisfaction have a significant effect on customer loyalty together. The Rsquare number of 0.358 indicates that 35.8 percent of the variation in customer loyalty can be explained by one independent variable used in the regression equation. While the remaining 64.2 percent is explained by other variables outside the variables used in this study

Robustness Test of the Two Stage K-L Estimator in Models with Multicollinear Regressors and Autocorrelated Error Term

In a classical multiple linear regression analysis, multicollinearity and autocorrelation are two main basic assumption violation problems. When multicollinearity exists, biased estimation techniques such as Maximum Likelihood, Restricted Maximum Likelihood and most recent the K-L estimator by Kibria and Lukman [1] are preferable to Ordinary Least Square. On the other hand, when autocorrelation exist in the data, robust estimators like Cochran Orcutt and Prais-Winsten [2] estimators are preferred. To handle these two problems jointly, the study combines the K-L with the Prais-Winsten’s two-stage estimator producing the Two-Stage K-L estimator proposed by Zubair & Adenomon [3]. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) criterion was used to compare the performance of the estimators. Application of the estimators to two (2) real life data set with multicollinearity and autocorrelation problems reveals that the Two Stage K-L estimator is generally the most efficient.

Pengaruh Motivasi Dan Pengetahuan Terhadap Minat Berinvestasi Saham Di Pasar Modal Syariah

This study aimed to determine the influenced of motivation and knowledge of students the study program Islamic banking on invest interest stock in the Islamic Capital Market. The sample in this research is 67 students. The test of the instrument was analyzed by using validity test and reliability test. The analysis technique used in this study is the descriptive statistics, classic assumption test and multiple linear regression. The classical assumption test consists of, normality test, multicollinearity test, heteroscedasticity test and autocorrelation test. Hypothesis test use the determination test, F test, and t test. The results showed that, motivation partially has an insignificant positive influence on invest interest stock in the Islamic Capital Market. Knowledge partially had a significant positive influence on invest interest stock in the Islamic Capital Market. Meanwhile, simultaneously based on the results of the F statistical test, the independent variables of motivation and knowledge together have a significant and positive influence on the dependent variable invest interest stock. The predictive ability of these two variables on invest interest stocks is 46.0%, while the remaining 54.0% is influenced by other variables outside the regression model.

PENGARUH GOOD CORPORATE GOVERNANCE TERHADAP PROFITABILITAS PERUSAHAAN PADA PT. BANK CENTRAL ASIA, TBK PERIODE TAHUN 2015-2020

This research aims to examine the effect of Good Corporate Governance (Institutional Ownership and Board of Directors) on Profitability at PT. Bank Central Asia, Tbk for the period 2015-2020. This research uses secondary data from PT. Bank Central Asia, Tbk in 2015- 2020. The data analysis technique used is the classical assumption data and multiple linear regression test. The results of this research indicate that institutional ownership and the board of directors have no significant effect on profitability.

Pengaruh Profitabilitas, Kinerja Keuangan, Ukuran Perusahaan dan Solvabilitas terhadap Pertumbuhan Laba

To see and test whether the effect of profitability, financial performance, company size and solvency on profit growth (empirical study of the trade, service and investment sector) listed on the IDX for the period 2017-2019 is the purpose of this study. The data analysis method used is statistical analysis method and quantitative descriptive approach. This research is an explanation level research. A total of 177 companies are the total population obtained from companies listed on the IDX from 2017-2019. Samples were taken using purposive sampling technique. This technique is often used by other researchers because the samples obtained are early for the purpose of the study. Samples can be obtained from 14 companies. Data analysis used several analyzes, namely classical assumption test and multiple linear regression. This study shows that partially the profitability variable that has an effect on profit growth, while the financial performance, company size, and solvency variables do not have a significant effect on service companies listed on the Indonesia Stock Exchange. Simultaneously profitability, financial performance, company size and solvency also have no effect on profit growth in trade, services and investment sector service companies listed on the Indonesia Stock Exchange.

Analysis of The Exchange Rate on The Thai Baht Against The Chinese Yuan Using A Support Vector Machine and Firefly Algorithm

This research constructed an optimal model to forecast the exchange rate of the Thai Baht against the Chinese Yuan by Support Vector Machine model and firefly algorithm. The software used for the construction was R program. The data used for modelling was secondary data collected from Bank of Thailand and the Ministry of Commerce, which consisted of the exchange rate of the Thai Baht against the Chinese Yuan, policy interest rate (per year), the Thai Baht index, import value, export value and international reserve fund. The collection of data was monthly records starting from January 2009 to June 2019, 126 data sets. The first 120 data sets were used for constructing the model and the last 6 data sets were used to verify the model. It was found that there were 3 factors which affected the exchange rate of the Thai baht against the Chinese yuan, with 5% statistical significance. The same direction factors were policy interest rate and import value and the opposite direction factor was international money fund. The optimal support vector machine model obtained was eps-regression type with radial basis function kernel, which had gamma parameter , epsilon parameter and cost value parameter . The model verification showed that the obtained model provided the root mean square error 0.1518 only whereas the classical multiple linear regression model provided the root mean square error 0.2614.

Research on Mathematical Model of Box Office Forecast through BP Neural Network and Big Data Technology

With the rapid development of China’s film industry, the public’s enthusiasm for watching films has growing, and the contribution rate of the film industry to the economy has increasing year by year. Based on the research at home and abroad, this paper takes the box office of China’s films as the dependent variable, selects a series of indicators of China’s film box office for grey correlation analysis, and selects seven related indicators as independent variables, including the number of fans of heroine, the number of fans of hero, the box office of the first week, the number of fans of director, Baidu Index, average ticket price and film type. The influencing factors calculated by grey correlation degree are taken as the input of BP neural network, and the box office of China’s films is taken as the output of the network to predict the box office of China’s films. The results show that the prediction model based on BP neural network is more accurate than the classical Multiple Linear Regression model. This model can predict to a certain extent, and the prediction effect is good. It can predict the overall trend of film box office and provide reference for investment enterprises.

PENGARUH PENGARUH KOMITE AUDIT, DEWAN KOMISARIS DAN DEWAN DIREKSI TERHADAP KINERJA KEUANGAN PERUSAHAAN

This study aims to examine the effect of the audit committee, the board of commissioners and the board of directors on the company's financial performance. This study aims to prove this influence on the company's success rate in carrying out its operational activities. The population in this study are manufacturing companies in the consumer goods sector listed on the IDX in 2017-2019. The sampling method uses purposive sampling, which is a sampling technique with certain considerations. The research sample was 23 in 3 years with a total of 69 samples. The data analysis method used classical assumption test and multiple linear regression.
 The results of this study indicate that the board of commissioners has a significant effect on the company's financial performance, meaning that a large size of the board of commissioners causes better management monitoring and can minimize fraud in the company so that the company's financial performance is getting better. The audit committee and the board of directors have no significant effect on the company's financial performance. This shows that the size or size of the audit committee does not guarantee the effectiveness of the audit committee's performance in supervising the company's financial performance, and the size of the board of directors does not guarantee the effectiveness of carrying out its responsibilities in managing the company.
 Keywords: audit committee, board of commissioners, board of directors, financial performance

Pengaruh Cash Ratio (CR), Total Asset Turnover (TATO), Return On Assets (ROA) Terhadap Dividend Payout Ratio (DPR) Pada Perusahaan Sektor Aneka Industri dan Industri Barang Konsumsi yang Terdapat di Bursa Efek Indonesia Tahun 2013-2017

This study aims to determine the effect of Cash Ratio, Total Asset Turnover and Return On Assets on Dividend Payout Ratio in the Various Industries and Consumer Goods Industry found on the Indonesian Stock Exchange. The populations of this study were all companies in the various goods industries and consumer goods industry in the Indonesia Stock Exchange of 85 companies in the period 2013-2017. The technique of determining the sample used is purposive sampling. The samples obtained are 23 companies incorporated in the various goods industry and consumer goods industry sectors in the Indonesia Stock Exchange for the period 2013-2017. The data analysis technique used is the classic assumption test and multiple linear regression analysis with the help of SPSS 20. Based on the analysis, the results show that Cash Ratio and Total Asset Turnover have no significant effect on Dividend Payout Ratio, while Influential Return On Assets gives positive and significant effect on the Dividend Payout Ratio.Keywords : Cash Ratio, Total Asset Turnover, Sales, Return On Assets, Dividend Payout Ratio

In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches

Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (KPE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (KPE-W), LDPE-air (KPE-A) and LDPE-seawater (KPE-SW) partition coefficients. These developed models have satisfying predictability (R2adj: 0.805–0.966, 0.963–0.991 and 0.817–0.941; RMSEtra: 0.233–0.565, 0.200–0.406 and 0.260–0.459) and robustness (Q2ext: 0.840–0.943, 0.968–0.984 and 0.797–0.842; RMSEext: 0.308–0.514, 0.299–0.426 and 0.407–0.462) in three datasets (water, air and seawater), respectively.In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown KPE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.

PURCHASE DECISION BASED ON THE BRAND IMAGE, PRODUCT DESIGN AND LIFESTYLE ON CONVERSE SHOES IN SURAKARTA

The purpose of this study was to determine the effect of brand image, product design and lifestyle on purchasing decisions for Converse shoes in Surakarta. This type of research uses quantitative descriptive research. The variables in this study were a brand image, product design and lifestyle. The population of this study was all people of Surakarta City, especially those who wear Converse shoes. The research sample consisted of 100 people, using purposive sampling technique, and data collection using a questionnaire. The data analysis technique used in this research is the coefficient of determination analysis technique, the classical assumption test and multiple linear regression. The test results of multiple linear regression analysis show that brand image has no significant effect on purchasing decisions, product design has a significant effect on purchasing decisions, and lifestyle has a significant effect on purchasing decisions.
 
 Keywords: Brand.Image, Product.Design,.Lifestyle

Pengaruh Motivasi Kerja dan Komitmen Organisasi Terhadap Kinerja Karyawan PT. BPR Syariah Lantabur Tebuireng di Jombang

Good employee performance must be maintained to complete duties and responsibilities for the creation of company goals, where work motivation and organizational commitment are the driving force. This research uses quantitative research and causal types. The population of this research is all employees of PT. BPRS Lantabur Tebuireng in the Jombang area. The tests used are: hypothesis test, classic assumption test, R2 test and multiple linear regression analysis. The results showed that: 1) work motivation partially has no effect on employee performance, resulting in a probability value of 0.143. 2) organizational commitment partially affects employee performance, resulting in a probability value of 0.00. 3). Meanwhile, work motivation and organizational commitment have a simultaneous effect on employee performance, with a probability value of 0.00.

Kepribadian, Motivasi, Dukungan Keluarga dan Pendidikan terhadap Minat Berwirausaha Mahasiswa Jurusan Akuntansi

This research was conducted to determine the influence of personality, motivation consisting of career and economic motivation, family support, accounting education and entrepreneurship education on the interest in entrepreneurship in regular accounting students majoring in the Faculty of Economics and Business, Udayana University. The population in this study were students majoring in regular accounting, class 2016. Methods of determining the sample using saturated sampling technique. The sample in this study amounted to 120 people. The data collection method is a survey method with a questionnaire. The data analysis technique is the classical assumption test and multiple linear regression analysis. Based on the results of the analysis, it was found that personality, motivation which includes career and economic motivation, family support, accounting education, and entrepreneurship education have a positive effect on students interest in entrepreneurship in the Bukit regular accounting major.
 Keywords: Personality; Motivation; Endorsement; Education; Entrepreneurship.

Lasso Regression for Daily Rainfall Modeling at Citeko Station, Bogor, Indonesia

Abstract Rainfall is one of the climate components which is very important for an agrarian country with a tropical climate like Indonesia. There are so many variables that can affect the intensity of rainfall, including humidity, air temperature, speed and wind direction. However, not all of these variables can have a significant effect, therefore, we need a modeling technique that can select and shrink the predictor variables so as to make the model simpler. One of the techniques used is the Lasso (least absolute shrinkage and selection operator). The purpose of this study is to model rainfall intensity using lasso regression and determine which variable have the greatest influence on rainfall intensity. This paper also designs an application system that can be used to implement Lasso regression. The response variables in the form of daily rainfall at the Citeko observation station, Bogor Indonesia is assumed to have normal distribution. Rainfall intensity modeling involves 16 predictor variables, namely the maximum temperature, minimum temperature, average temperature, average humidity, sun exposure, maximum wind speed, wind direction when maximum wind speed and 8 other categorical variables are the most frequent wind direction. The resulting Lasso regression model successfully select and shrink the variables used to 9 variables only. In addition, based on the AIC and coefficient of determination indices for the model evaluation, the Lasso regression shows satisfying results rather than classical multiple linear regression. In Lasso regression, AIC value is 5901.20 where in classical model it is 5911.43. Also, Lasso regression coefficient of determination value is greater that is 22.73% while in classical model is 21.41%. From lasso regression model that has been obtained, it is known that the most frequent wind direction to the north is the variable that has the greatest influence on rainfall intensity. The Lasso regression application has been successfully created so that it can facilitate the user in uploading data, exploration of data and variables, Lasso regression analysis and report result.

PENGARUH KEPEMIMPINAN DIREKTIF, PENINGKATAN SUMBER DAYA MANUSIA DAN PERATURAN MENTERI PERHUBUNGAN NOMOR 78 TAHUN 2017 TERHADAP MOTIVASI KERJA KARYAWAN PADA PT. BATAM TEKNIK DI LINE MAINTENANCE BANDAR UDARA INTERNASIONAL KUALANAMU

This study aims to examine and analyze the effect of directive leadership, human resource improvement and The Minister of Transportation Regulation Number 78 Year 2017 on employee motivation at Kualanamu International Airport Line Maintenance of PT. Batam Teknik. Data collection was carried out by distributing questionnaires to 72 employees of Kualanamu International Airport Line Maintenance of PT. Batam Teknik. Data analysis in this study used SPSS version 22. The sampling technique used is the census method and data testing techniques used in this study include validity test, reliability test, classical assumption test and multiple linear regression analysis used to test and prove the research hypothesis. The results of the analysis show that directive leadership, increased human resources and The Minister of Transportation Regulation Number 78 Year 2017 have a positive and significant effect on employee work motivation simultaneously. This result can be seen from the calculated value of 31,587 with a significance value of 0.000 smaller than the significance limit of 0.050. The influence of directive leadership, increased human resources The Minister of Transportation Regulation Number 78 Year 2017 on employee motivation of 56.4% while the remaining 43.6% is explained by other factors outside of this study. Directive leadership, improvement of human resources and The Minister of Transportation Regulation Number 78 Year 2017 each partially affects employee motivation. This result can be seen from the calculated value of the directive leadership variable of 2.363with a significance value of 0.021 less than 0.050, and the calculated value of the human resource improvement variable of 2,089with a significance value of 0.040 less than 0.050 and the calculated value of the variable of The Minister of Transportation Regulation Number 78 Year 2017 is 2,012with a significance value of 0.048 less than 0.050.

Estimating Pole Capacity From Radio Network Performance Statistics by Supervised Learning

Network dimensioning is a critical task for cellular operators to avoid degraded user experience and unnecessary upgrades of network resources with changing mobile traffic patterns. For this purpose, smart network planning tools require accurate cell and user capacity estimates. In these tools, throughput is often used as a capacity metric due to its close relationship with user satisfaction. In this work, a comprehensive analysis is carried out to compare different well-known Supervised Learning (SL) algorithms for estimating cell and user throughput in the DownLink in busy hours from radio measurements collected on a cell basis in the Operation Support System (OSS). The considered SL approaches include random forest, shallow multi-layer perceptron, support vector regression and k-nearest neighbors. Such algorithms are compared with classical multiple linear regression and deep learning approaches considered in previous works. All these algorithms are tested in two radio access technologies: High Speed DownLink Packet Access (HSDPA) and Long Term Evolution (LTE). To this end, two datasets with the most relevant performance indicators per technology are collected from live cellular networks. Results show that non-deep SL algorithms are the most appropriate option for applications with storage constraints, such as network planning tools, since they provide a higher accuracy with reduced datasets.