scholarly journals The Factors Affecting Land Prices In Housing Location In Sidoarjo Regency

2017 ◽  
Vol 1 (1) ◽  
pp. 37-47
Author(s):  
Partomi Simangunsong ◽  
Arasy Alimudin ◽  
Muh. Barid Nizaruddin Wajdi
Keyword(s):  
Residential Location ◽  
Least Square ◽  
City Center ◽  
Sale Price ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Road Width ◽  
Log Linear ◽  
Road Distance

The need for residential location is one of the basic needs of the community and the attractiveness of the residential location is a unique feature where this feature is not made by the respective occupants, but by external factors from the residential environment in the area. This study aims to analyze the factors that are considered as the basis that affect the price of land. This research uses quantitative approach with associative research method. Linear analysis with quadratic method. Ordinary Least Square (OLS). From the analysis of this research model obtained log-linear F-accounting 70,162 while the value of F-table (0,05; 5,48) is 2,45. because F-count> F-table, Ho means rejected and explanatory variables include Distance to city center, Distance to main road, Distance to toll gate, Road width, and security simultaneously can be explained significantly at land sale price.

scholarly journals Estimating a global demand model for soybean traffic through the Panama Canal

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Javier Ho ◽  
Paul Bernal
Keyword(s):  
Pacific Northwest ◽  
Least Square ◽  
Panama Canal ◽  
Demand Model ◽  
Department Of Agriculture ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Global Demand ◽  
Alternative Routes ◽  
The U.S

AbstractThis study attempts to fit a global demand model for soybean traffic through the Panama Canal using Ordinary Least Square. Most of the soybean cargo through the interoceanic waterway is loaded on the U.S. Gulf and East Coast ports -mainly destined to East Asia, especially China-, and represented about 34% of total Panama Canal grain traffic between fiscal years 2010–19. To estimate the global demand model for soybean traffic, we are considering explanatory variables such as effective toll rates through the Panama Canal, U.S. Gulf- Asia and U.S. Pacific Northwest- Asia freight rates, Baltic Dry Index, bunker costs, soybean export inspections from the U.S. Gulf and Pacific Northwest, U.S. Gulf soybean basis levels, Brazil’s soybean exports and average U.S. dollar index. As part of the research, we are pursuing the estimation of the toll rate elasticity of vessels transporting soybeans via the Panama Canal. Data come mostly from several U.S. Department of Agriculture sources, Brazil’s Secretariat of Foreign Trade (SECEX) and from Panama Canal transit information. Finally, after estimation of the global demand model for soybean traffic, we will discuss the implications for future soybean traffic through the waterway, evaluating alternative routes and sources for this trade.

scholarly journals Determinants of Household Debt in Malaysia from the year 2010 to 2017

2019 ◽  
Vol 5 (2) ◽  
pp. 91
Author(s):  
Zahariah Mohd Zain ◽  
Nurul Ainun Ahmad Atory Ahmad Atory ◽  
Sarah Amirah Hanafi
Keyword(s):  
Positive Relationship ◽  
Negative Relationship ◽  
Housing Price ◽  
Least Square ◽  
Household Debt ◽  
Macroeconomic Variables ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Lending Rate

Household debt has become an issue in the Malaysian economy as it affects the country socially and economically.This study aims to examine the determinants of household debt from the year 2010 until 2017. This study employs the Ordinary Least Square (OLS) method and the macroeconomic variables used in this study are Gross Domestic Product (GDP), base lending rate, unemployment and housing price as independent variables. The results indicate that the trend of household debt in Malaysia has shown a continuous rise from the year 2010 to 2017. GDP, base lending rate and housing price indicate a positive relationship towards household debt while unemployment shows a negative relationship to household debt in Malaysia. All explanatory variables have shown a significant relationship except for GDP. Housing price has been found to be the most significant factor and positively related to household debt. The findings indicate that the higher the price of houses, the higher the household debt will be.

scholarly journals A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers

2020 ◽  
Vol 12 (18) ◽  
pp. 7688
Author(s):  
Fan Yang ◽  
Linchao Li ◽  
Fan Ding ◽  
Huachun Tan ◽  
Bin Ran
Keyword(s):  
Cellular Phone ◽  
Test Methods ◽  
Least Square ◽  
Data Driven ◽  
Urban Residents ◽  
Trip Generation ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Data Driven Approach ◽  
Non Local

Trip generation modeling is essential in transportation planning activities. Previous modeling methods that depend on traditional data collection methods are inefficient and expensive. This paper proposed a novel data-driven trip generation modeling method for urban residents and non-local travelers utilizing location-based social network (LBSN) data and cellular phone data and conducted a case study in Nanjing, China. First, the point of interest (POI) data of the LBSN were classified into various categories by the service type, then, four features of each category including the number of users, number of POIs, number of check-ins, and number of photos were aggregated by traffic analysis zones to be used as explanatory variables for the trip generation models. We used a random tree regression method to select the most important features as the model inputs, and the trip models were established based on the ordinary least square model. Then, an exploratory approach was used to test the performance of each combination of the variables with various test methods to identify the best model for residents’ and travelers’ trip generation functions. The results suggest land use compositions have significant impact on trip generations, and the trip generation patterns are different between urban residents and non-local travelers.

scholarly journals The Efficiency of Farmers Group Association on Strengthening The Institutions of Community Food Distribution Program Stage in Yogyakarta Special Region 2014

2017 ◽  
Vol 2 (1) ◽  
pp. 035
Author(s):  
Eny Ivan's ◽  
Jangkung Handoyo Mulyo ◽  
Dwidjono Hadi Darwanto
Keyword(s):  
Secondary Data ◽  
Food Distribution ◽  
Least Square ◽  
Data Envelopment ◽  
Factors Affecting ◽  
Ordinary Least Square ◽  
The Government ◽  
Agriculture And Food ◽  
Group Association ◽  
Special Region

In protecting and empowering the farmers, farmers group, and farmers group association (Gapoktan) from falling prices of grain and rice at harvest time and food accessibility problems, the government through the Ministry of Agriculture and Food Security Agency implemented the Strengthening the Institutions of Community Food Distribution Program (Strengthening-LDPM). This research was aimed to analyse the level of efficiency and to identify factors influencing the efficiency of Gapoktan in implementing the Strengthening-LDPM by involving 40 Gapoktan post-independence. The data used in this research were primary and secondary data, drawn from stockopname reports in 2014. This research used DEA (Data Envelopment Analysis) analysis, assuming that CRS (Constant Return to Scale) and VRS (Variable Return to Scale) using output-oriented assumptions. In addition, factors affecting the efficiency were analysed using multiple regression OLS (Ordinary Least Square). Based on DEA-CRS approach, as much as 37.5% Gapoktan were efficient and 62.5% Gapoktan were inefficient. Whereas with the approach of the DEA-VRS, 50% Gapoktan were efficient and 50% Gapoktan were inefficient. The average age of Gapoktan board, total volume of grain or rice sales, total volume of food reserve, and total loan interest affect significantly in increasing the efficiency of Gapoktan in running the strengthening-LDPM Program.

scholarly journals Solving Multicollinearity Problem in Linear Regression Model: The Review Suggests New Idea of Partitioning and Extraction of the Explanatory Variables

2021 ◽  
Vol 2 (1) ◽  
pp. 12-20
Author(s):  
Kayode Ayinde, Olusegun O. Alabi ◽  
Ugochinyere Ihuoma Nwosu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Real Data ◽  
Least Square ◽  
Model Parameters ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Wide Range ◽  
High Level

Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused on developing estimators regardless of effect of differences in levels of multicollinearity among regressors. Studies have considered single-estimator and combined-estimator approaches without sustainable solution to multicollinearity problems. The possible influence of partitioning the regressors according to multicollinearity levels and extracting from each group to develop estimators that will estimate the parameters of a linear regression model when multicollinearity occurs is a new econometrics idea and therefore requires attention. The results of new studies should be compared with existing methods namely principal components estimator, partial least squares estimator, ridge regression estimator and the ordinary least square estimators using wide range of criteria by ranking their performances at each level of multicollinearity parameter and sample size. Based on a recent clue in literature, it is possible to develop innovative estimator that will sustainably solve the problem of multicollinearity through partitioning and extraction of explanatory variables approaches and identify situations where the innovative estimator will produce most efficient result of the model parameters. The new estimator should be applied to real data and popularized for use.

scholarly journals FACTORS AFFECTING INFANT MORTALITY RATE IN KARANGASEM, BALI

2019 ◽  
Vol 4 (1) ◽  
pp. 12-15
Author(s):  
Ni Luh Putu Suciptawati ◽  
Ni Luh Putu Suciptawati ◽  
Made Asih ◽  
Kartika Sari ◽  
I G A M Srinadi
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Linear Model ◽  
Birth Spacing ◽  
Linear Analysis ◽  
Infant Mortality Rate ◽  
Categorical Variables ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Log Linear

The purpose of this study was to determine the factors that influence the infant mortality rate in Karangasem, Bali. The method used in this research is the Log Linier model. In the Log linear model analyze relationship pattern among group of categorical variables which include an association of two or more variables, either simultaneously or partially. A Patterned relationship between variables can be seen from the interaction between variables. Log linear analysis does not distinguish between explanatory variables and response variables. The population in this study was all babies born in Karangasem in 2015 that is as many as 7,895 babies with live birth status and as many as 7,835 babies and 60 infants died. As a sample, 100 babies were taken, of which 60 were live and 40 died. The results show that infant mortality is affected by infant weight, how old the mother during childbirth, and interaction between birth spacing and infant weight  

scholarly journals Analysis of Factors Affecting the Demand of Chicken Egg’s in the Traditional Market of Medan City

2017 ◽  
Vol 5 (2) ◽  
Author(s):  
Dwi Andini Puspita Sari br Sinaga ◽  
Armyn Hakim Daulay ◽  
Edhy Mirwandhono ◽  
Sayed Umar ◽  
Iskandar Sembiring
Keyword(s):  
Least Squares Method ◽  
Secondary Data ◽  
Least Square ◽  
Primary Data ◽  
Factors Affecting ◽  
Ordinary Least Square ◽  
On Demand ◽  
Chicken Egg ◽  
Traditional Market ◽  
Chicken Eggs

The development of the society resulting for animal protein needed such as chicken egg’s increased and affect the demand for eggs in Medan. Therefore, it is necessary to do research to know the factors that influence the demand of chicken egg in traditional market of Medan city at consumer level by using Ordinary Least Square (OLS) method or least squares method with SPSS 22.0 tool. This study was conducted from May to June 2017. This study used primary data obtained from observations and interviews of respondents. The location of the research is determined purposively and the respondent determination by accidental method. Primary data was obtained from 90 consumers of chicken eggs and added with secondary data from government agencies. Then it was analyzed by multiple linear analysis with 5 demand variables namely, the number of dependents, education, income, egg price of chicken, and age. The results showed that all variables simultaneously had a significant effect on demand. Partially only variable of dependent which have real effect to demand of chicken egg of race. So it can be concluded that the demand for eggs in Medan is only influenced by the number of dependents

scholarly journals ANALISIS FAKTOR - FAKTOR YANG MEMPENGARUHI HARGA TANAH UNTUK PENGGUNAAN PERUMAHAN (STUDI KASUS: KECAMATAN BANYUMANIK)

2017 ◽  
Vol 5 (1) ◽  
pp. 57
Author(s):  
Triana Kurniwati ◽  
Bagio Mudakir
Keyword(s):  
Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Least Square Method ◽  
Research Area ◽  
Least Square ◽  
City Center ◽  
Land Price ◽  
Ordinary Least Square ◽  
The City

Semarang city is densely populated that demand of settlement will increase continually, but land in city center is very limited and even it is scarce, therefore the land price which is placed in city center is high. That is why many inhabitant of Semarang city prefer to live in outskirts of the city. The shifting of land demand to the outskirts is also followed by increasing of land price in outskirts, it causes the land price in outskirts is uncontrolled.The research takes location in Banyumanik area. This research area consists of 7 districts, that are Jabungan, Pudak Payung, Banyumanik, Srondol Kulon, Pedalangan, Ngesrep, and Gedawang district. The sample total is one hundred (100). The data is analyzed by using multiple linear regression model with ordinary least square method (OLS).

scholarly journals Analisis Pertumbuhan Ekonomi Daerah Provinsi Jambi

2018 ◽  
Vol 9 (1) ◽  
pp. 22
Author(s):  
Nurdin Nurdin
Keyword(s):  
Economic Growth ◽  
Capital Expenditure ◽  
Secondary Data ◽  
Regression Equation ◽  
Least Square ◽  
Analysis Tool ◽  
Working Population ◽  
Factors Affecting ◽  
Ordinary Least Square ◽  
Investment Capital

This study uses secondary data collected by the object of research in Jambi Province in the form of factors affecting the economic growth of Jambi Province sourced from the Central Bureau of Statistics (BPS). Data were collected during the period 2004 to 2015. The purpose of this study is to analyze and know what factors affect the economic growth of Jambi Province period 2004-2015. The analytical tool used is this research using econometric analysis tool with Ordinary Least Square (OLS) method with multiple linear regression equation through the aid of SPSS software program. 21:00. Based on the discussion of data analysis results in this study, it can be concluded the result of R-squared calculation shown in the above equation obtained R2 value of 0.989. This shows that about 98.90 percent of the upturned economic growth (Yt) in Jambi Province is influenced by investment variable (X1t), capital expenditure (X2t), working population (X3t), unemployment (X4t) and poverty (X5t). While the remaining 1.10 percent, explained by other variables that are not included into the regression equation. Keywords: Economic Growth, Investment, Capital Expenditure, Working Population, Unemployment And Poverty

scholarly journals Assessing factors affecting FDI in developing Asian countries

2021 ◽  
Vol 9 (2) ◽  
pp. 16-28
Author(s):  
P. Gupta
Keyword(s):  
Random Effect ◽  
Least Square ◽  
Market Size ◽  
Asian Countries ◽  
Factors Affecting ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Fixed Capital Formation ◽  
Effect Regression ◽  
Independent Variable

The paper focuses on various factors that affect the inflow of Foreign Direct Investment in developing countries. The study majorly deals with Asian countries, namely India, China, Myanmar, Nepal, Pakistan, Bangladesh and Bhutan, that are progressing from being aid-dependent to trading giants. The factors affecting FDI are majorly categorised into dependent and independent variables. Here, in this study, the dependent variable considered is FDI inflow, and independent variables are market size, the value of the currency, export, import, gross fixed capital formation, GDP deflator, cost of borrowing and economic reforms. Pooled Ordinary Least Square (OLS), fixed effect and random effect regression analysis is done to ascertain the best regression model and various tests are performed to check the intensity of effect caused by each independent variable on our dependent variable.

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