scholarly journals Analysis of Factors Affecting the Competitiveness of Indonesian Crude Palm Oil (CPO) Export in the Global Market

2020 ◽  
Vol 2 (3) ◽  
pp. 97-110 ◽  
Author(s):  
Mirawati Yanita ◽  
Dompak MT Napitupulu ◽  
Karina Rahmah
Keyword(s):  
Linear Regression ◽  
Soybean Oil ◽  
Multiple Linear Regression ◽  
Oil Palm ◽  
Multiple Linear Regression Model ◽  
Oil Prices ◽  
Global Market ◽  
Factors Affecting ◽  
Oil Palm Plantation ◽  
Export Volume

This study aims to: (1) Analyze the development of Indonesian CPO export competitiveness in the global market from 1998 to 2017 (2) Analyze factors affecting the competitiveness of Indonesian CPO export in the global market from 1998 to 2017. The data used is time series spanning 20 years (1998-2017). The competitiveness of Indonesian CPO export in the global market is presented descriptively in the form of images, while affecting factors are analyzed using a multiple linear regression model. The results show that Indonesian CPO has comparative and competitive competitiveness in the global market. The results of multiple linear regression tests indicate that the factors that comparatively affect the competitiveness of Indonesian CPO exports in the global market are domestic CPO production, global CPO prices and petroleum prices while factors that competitively affect competitiveness are ​​oil palm plantation area, Malaysian export volume, soybean oil price and exchange rates. Factors that positively impact Indonesian CPO competitiveness are domestic CPO production, oil prices and the ​​oil palm plantation area. Improving these variables will increase the competitiveness of Indonesian CPO exports, while an increase in global CPO prices, soybean oil prices and the exchange rate will reduce the competitiveness of CPO exports. The variable that has no significant effect is the export volume of Malaysia.

scholarly journals Housing Price Prediction Based on Multiple Linear Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qingqi Zhang
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Housing Prices ◽  
Housing Price ◽  
Multiple Linear Regression Model ◽  
Data Set ◽  
Factors Affecting ◽  
Price Prediction

In this paper, the author first analyzes the major factors affecting housing prices with Spearman correlation coefficient, selects significant factors influencing general housing prices, and conducts a combined analysis algorithm. Then, the author establishes a multiple linear regression model for housing price prediction and applies the data set of real estate prices in Boston to test the method. Through the data analysis and test in this paper, it can be summarized that the multiple linear regression model can effectively predict and analyze the housing price to some extent, while the algorithm can still be improved through more advanced machine learning methods.

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

Jurnal Varian ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 1-8
Author(s):  
Evellin Dewi Lusiana ◽  
Arief Darmawan ◽  
Sarah Hutahaean ◽  
Muhammad Musa ◽  
Mohammad Mahmudi ◽  
...  
Keyword(s):  
Linear Regression ◽  
Dissolved Oxygen ◽  
Spatial Autocorrelation ◽  
Multiple Linear Regression ◽  
Control Variable ◽  
Multiple Linear Regression Model ◽  
Spatial Filtering ◽  
Factors Affecting ◽  
Model Free ◽  
Nitrate And Nitrite

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.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN PROLL TAPE PADA UD. PRIMADONA JEMBER

2016 ◽  
Vol 15 (2) ◽  
Author(s):  
Shanti Maulina Ningrum ◽  
Ratih Puspitorini Y. A. ◽  
Wenny Dhamayanthi
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Regression Testing ◽  
Purchase Decision ◽  
Purchasing Decisions ◽  
Validity And Reliability ◽  
Statistical Software ◽  
Factors Affecting ◽  
Independent Variables

This research title is Factors - Factors Affecting Purchase Decision  Proll Tape in UD. Primadona Jember .The purpose of this research is to analyze and test the effect of independent variables simultaneously, partially and the variables that affect the dominant on purchasing decisions proll tape. This study took a sample of 40 respondents. Data were analyzed using statistical software SPSS version 16 with multiple linear regression model. Before analyzing the multiple linear regression to tested the validity and reliability. The results of the research conducted showed that regression testing F test showed that all independent variables have a significant effect on purchasing decisions. Regression testing test showed that the independent variables that affect the purchasing decisions of the variable packaging. While independent variables were not significant effect of variable price and taste. Dominant variable in affecting purchasing decisions in UD. Primadona is variable packaging.

scholarly journals Factors Affecting the Productivity of the Big Onion in Hambantota District during the Off Season

2019 ◽  
pp. 1-10
Author(s):  
M. K. M. Sulochana ◽  
L. S. Nawarathna
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Factors Affecting ◽  
Absolute Percentage Error ◽  
Average Productivity

Aim: The main aim of this study is to identify the factors affecting the big onion productivity of Hambantota district during the off-season. Moreover, we identify the average productivity per acre from Hambantota district and compare it with the other areas that cultivated the big onion. Further, identify the main issues encountered in big onion cultivation in Hambantota and identify the critical contributing factors for the big onion cultivation in this area. Place and Duration of Study: During the off seasons in 2015 to 2016 in Hambantota District. Methodology: Sample data was collected from 201 farmers in Hambantota district. Multiple linear regression model was used to identify the factors affecting the big onion productivity in Hambantota district during the off-season. The normality assumption of the regression model was checked using Kolmogorov–Smirnov test, Shapiro Wilk normality test and Skewness and Kurtosis test. Pearson, Spearman’s Rank and Partial correlation tests were used to check the correlations between variables. Mean absolute percentage error (MAPE) and Symmetrical Mean absolute percentage error (SMAPE) values were used to validate the fitted model. Results: By the multiple linear regression model main factors affecting the productivity of big onion in Hambantota area were Seasonal Months, Monthly Income, Subsidies Fertilizer and Cultivated Quantity. And the R-squared value was most like to 80% and this means these independent variables were described 80% of the dependent variable.  Model accuracies were reported as 98.48% and 98.49% from MAPE and SMAPE respectively. Therefore, this multiple linear regression model was suitable for this study. Further, the model determined the affected factors for the big onion cultivation in Hambantota district during the off-season. Conclusion: Hambantota district average productivity was less than other areas. Big onion productivity of Matale is more than 2 times greater than big onion productivity of Hambantota. Off season big onion cultivation in Hambantota district is not very effective because of the average productivity is less than other areas in Sri Lanka.

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.

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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