scholarly journals Faktor-Faktor Yang Mempengaruhi Kinerja Bank Devisa Yang Terdaftar Di Bursa Efek Indonesia (2013-2017)

2018 ◽  
Vol 1 (2) ◽  
pp. 69-76
Author(s):  
Hartanti Hartanti
Keyword(s):  
Quantitative Analysis ◽  
Linear Regression ◽  
Foreign Exchange ◽  
Linear Regression Model ◽  
Stock Exchange ◽  
Multiple Linear Regression Model ◽  
Coefficient Of Determination ◽  
Performance Factors ◽  
Exchange Performance ◽  
Simultaneous Influence

Performance is a tool to evaluate the success of the company in running its business. This study aims to examine the performance factors of Foreign Exchange Banks Listed on the Indonesia Stock Exchange. Performance factors in this study were measured by the ratio of CAR, NPL, LDR, BOPO, GWM while Performance measured by ROA. This study uses quantitative analysis with multiple linear regression model to test the influence of these factors on the performance of foreign exchange banks simultaneously or partially. The results showed that there was a significant and simultaneous influence between CAR, NPL, LDR, BOPO, GWM on Foreign Exchange Bank ROA. Coefficient of determination simultaneously CAR, NPL, LDR, BOPO, GWM affect ROA of 85,6% and the rest influenced by other factors. Partially CAR, NPL and GWM have no significant effect on ROA of Foreign Exchange Bank. while the LDR and BOPO have a negative and significant effect on the ROA of Foreign Exchange Bank.

Whole Facility Measurement for Quantifying Energy Saving in an Office Building, Malaysia

2015 ◽  
Vol 785 ◽  
pp. 676-681 ◽  
Author(s):  
Nor Shahida Razali ◽  
Nofri Yenita Dahlan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Energy Saving ◽  
Linear Regression Model ◽  
Energy Use ◽  
Multiple Linear Regression Model ◽  
Office Building ◽  
Coefficient Of Determination ◽  
Independent Variable

This paper presents the concept of International Performance Measurement and Verification Protocol (IPMVP) for determining energy saving at whole facility level for an office building in Malaysia. Regression analysis is used to develop baseline model from a set of baseline data which correlates baseline energy with appropriate independents variables, i.e. Cooling Degree Days (CDD) and Number of Working Days (NWD) in this paper. In determining energy savings, the baseline energy is adjusted to the same set condition of reporting period using energy cost avoidance approach. Two types of energy saving analyses have been presented in the case study; 1) Single linear regression for each independent variable, 2) Multiple linear regression for each independent variable. Results show that NWD has coefficient of determination, R2 higher than CDD which indicates that NWD has stronger correlation with the energy use than CDD in the building. Finding also shows that the R2 for multiple linear regression model are higher than single linear regression model. This shows the fact that more than one component are affecting the energy use in the building.

scholarly journals PENGARUH INFLASI, BI RATE, KURS DAN INDEKS DOW JONES INDUSTRIAL AVERAGE TERHADAP JAKARTA ISLAMIC INDEX (JII)

Media Ekonomi ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 87
Author(s):  
Muhammad Rasyid Ridha ◽  
Harmaini Harmaini
Keyword(s):  
Exchange Rate ◽  
Linear Regression ◽  
Regression Model ◽  
Foreign Exchange ◽  
Significant Influence ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Analysis Method ◽  
Dow Jones Industrial Average ◽  
Research Show

<em>This research discusses the influence of inflation, BI Rate, Exchange rate (IDR/USD) and Dow Jones Industrial Average</em>. <em>The analysis method used is multiple linear regression model with α = 5%. With EViews 9.0 applications.</em> <em>The results of this research show that inflation, BI Rate, Foreign Exchange and Dow Jones Industrial Average simultaneously had significant influence towards on the Jakarta Islamic Index (JII). Meanwhile, partially Inflation had positive and significant influence towards on the JII. BI Rate partially had negative and significant influence towards on the JII. But Exchange rate (IDR/USD) partially do not influence on the JII and Dow Jones partially had positive and significant influence towards on the JII</em>.

scholarly journals SIMULATION AND LEARNING DYNAMICS IN BUSINESS GAMES

2017 ◽  
Vol 18 (2) ◽  
pp. 49-79 ◽  
Author(s):  
ADONAI JOSÉ LACRUZ
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Management Students ◽  
Degree Of Association ◽  
Methodological Aspects ◽  
Education In Brazil ◽  
Simultaneous Influence

ABSTRACT Purpose: This paper studies the influence of simulation dynamics in the learning of business games participants. Originality/gap/relevance/implications: Although many studies suggest the influence of factors linked simulation dynamics in the learning of participants of business games, it is unusual to investigate partial and simultaneous influence. Within lots of studies, the purpose is to evaluate the influence of one antecedent factor on learning of participants in business games. To explore possible inter-relationships between independent variables and effects of interaction expands the scope of analysis. Key methodological aspects: It was examined the statements of 90 undergraduate management students of four institutions of higher education in Brazil, by analysis through multiple linear regression model in six variables linked simulation dynamics (professor, manual, team, complexity, debriefing and duration). Summary of key results: Two variables composed the multiple linear regression model (debriefing and duration). These variables obtained a degree of association of 59% with the quantum of learning perceived by the participants of business games and explained 32% of its variation. Key considerations/conclusions: The findings of this study may contribute to elaborate lesson plans with business games describing the influence of factors linked simulation dynamics, many of them under the professor control.

scholarly journals The Analysis of the Influence Factors of China’s Outbound Tourism Market

2018 ◽  
Vol 228 ◽  
pp. 05004
Author(s):  
He Liu
Keyword(s):  
Exchange Rate ◽  
Quantitative Analysis ◽  
Linear Regression ◽  
Qualitative Analysis ◽  
Regression Model ◽  
Linear Regression Model ◽  
Influence Factors ◽  
Multiple Linear Regression Model ◽  
Literature Study ◽  
Econometric Methods

Outbound tourism gets special attention from tourism academic circles and tourism companies recently. And now China has become the world’s largest outbound tourism consumer. So this paper is written to research the influence factors of China’s outbound tourism market, providing reference for the study of China’s outbound tourism market. The literature study have found that the number of China’s outbound tourism related to the duration of their leisure trips, exchange rate, residents’ deposits and the number of tourism agency. By using of the econometric methods and R-studio, we build a multiple linear regression model. In the end, we get a conclusion by quantitative analysis of data and qualitative analysis of tourism’s law. We found that there are linear relations exists in logarithm of the number of China’s outbound tourism, the residents’ deposits ,the logarithm of duration of their leisure trips and the logarithm of number of tourism agency.

scholarly journals Pengaruh Ukuran Perusahaan, ROA, DAR, Dan Reputasi Auditor terhadap Audit Delay

2017 ◽  
Vol 1 (1) ◽  
pp. 23-34
Author(s):  
Mimelientesa Irman
Keyword(s):  
Linear Regression ◽  
Linear Regression Model ◽  
Stock Exchange ◽  
Manufacturing Sector ◽  
Multiple Linear Regression Model ◽  
Company Size ◽  
Manufacturing Companies ◽  
Independent Variables ◽  
Model Sample ◽  
Observation Period

   This study aims to analyze the factors that affect audit delay on manufacturing companies listed in Indonesia Stock Exchange. This test uses multiple linear regression model. Sample of this study are 20 companies in manufacturing sector. Observation period is 6 years from 2010 to 2015. Independent variables in this study consisted of company size, profitability, solvency, and auditors reputation which tested its influence on audit delay as dependent variable. The results showed that company size, profitability, solvency, and auditors reputation significantly influence on audit delay of manufacturing companies listed in Indonesia Stock Exchange period 2010 until 2015  Keywords: Firm size, ROA, DAR, Reputation Auditor and Audit Delay

scholarly journals Influence of social media advertising, e-marketing and product quality on the process of purchasing nature cosmetics

2019 ◽  
Vol 8 (5) ◽  
pp. 316-321
Author(s):  
Syawaluddin Syawaluddin ◽  
Joni Joni ◽  
Erwin Erwin
Keyword(s):  
Social Media ◽  
Linear Regression ◽  
Product Quality ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Coefficient Of Determination ◽  
Environment Friendly ◽  
Social Media Advertising ◽  
North Sumatra

This study seeks to investigate the role of Social Media Advertising, E-Marketing, and Product Quality on Consumers’ Decision to Purchase Nature Cosmetics in North Sumatra-Indonesia partially and simultaneously. The data were analyzed using multiple Linear Regression model and coefficient of Determination. This research leads to the results that variables of Social Media Advertising, E-Marketing, and Product Quality simultaneously have a positive and significant impact on consumers’ decision in making purchases of Nature Cosmetics in North Sumatra-Indonesia and partially the variable of product quality is more dominant in consumers’ decision in purchasing Environment-friendly Cosmetics in North Sumatra-Indonesia. The coefficient of determination (R Square) shows that the variable of Social Media Advertising, E-Marketing, Product Quality is 0,593 or 59.3% while the remaining 40,7% is affected by other factors beyond the scope of this study.

scholarly journals Forecasting the changes in daily stock prices in Shanghai Stock Exchange using Neural Network and Ordinary Least Squares Regression

2020 ◽  
Vol 17 (3) ◽  
pp. 292-307
Author(s):  
Sunantha Prime
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Stock Prices ◽  
Global Financial Crisis ◽  
Stock Exchange ◽  
Multiple Linear Regression Model ◽  
Ordinary Least Squares

The research focuses on finding a superior forecasting technique to predict stock movement and behavior in the Shanghai Stock Exchange. The author’s interest is in stock market activities during high volatility, specifically 13 years from 2002 to 2015. This volatile period, fueled by events such as the dot-com bubble, SARS outbreak, political leadership transitions, and the global financial crisis, is of interest. The study aims to analyze changes in stock prices during an unstable period. The author used advanced computer sciences, Machine Learning through information processing and training, and the traditional statistical approach, the Multiple Linear Regression Model, with the least square method. Both techniques are accurate predictors measured by Absolute Percent Error with a range of 1.50% to 1.65%, using a data file containing 3,283 observations generated to record the daily close prices of individual Chinese companies. The t-test paired difference experiment shows the superiority of Neural Network in the finance sector and potentially not in other sectors. The Multiple Linear Regression Model performs equivalent to the Neural Network in other sectors.

scholarly journals Estimation of Health-Related Physical Fitness Using Multiple Linear Regression in Korean Adults: National Fitness Award 2015–2019

2021 ◽  
Vol 12 ◽  
Author(s):  
Sung-Woo Kim ◽  
Hun-Young Park ◽  
Hoeryong Jung ◽  
Jinkue Lee ◽  
Kiwon Lim
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Cardiorespiratory Fitness ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Coefficient Of Determination ◽  
Muscular Endurance ◽  
Dependent Variables ◽  
Health Related

Continuous health care and the measurement of health-related physical fitness (HRPF) is necessary for prevention against chronic diseases; however, HRPF measurements including laboratory methods may not be practical for large populations owing to constraints such as time, cost, and the requirement for qualified technicians. This study aimed to develop a multiple linear regression model to estimate the HRPF of Korean adults, using easy-to-measure dependent variables, such as gender, age, body mass index, and percent body fat. The National Fitness Award datasets of South Korea were used in this analysis. The participants were aged 19–64 years, including 319,643 male and 147,600 females. HRPF included hand grip strength (HGS), flexibility (sit and reach), muscular endurance (sit-ups), and cardiorespiratory fitness (estimated VO2max). An estimation multiple linear regression model was developed using the stepwise technique. The outlier data in the multiple regression model was identified and removed when the absolute value of the studentized residual was ≥2. In the regression model, the coefficient of determination for HGS (adjusted R2: 0.870, P &lt; 0.001), muscular endurance (adjusted R2: 0.751, P &lt; 0.001), and cardiorespiratory fitness (adjusted R2: 0.885, P &lt; 0.001) were significantly high. However, the coefficient of determination for flexibility was low (adjusted R2: 0.298, P &lt; 0.001). Our findings suggest that easy-to-measure dependent variables can predict HGS, muscular endurance, and cardiorespiratory fitness in adults. The prediction equation will allow coaches, athletes, healthcare professionals, researchers, and the general public to better estimate the expected HRPF.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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