scholarly journals Analisis Masalah Heteroskedastisitas Menggunakan Generalized Least Square dalam Analisis Regresi

2019 ◽  
Vol 1 (2) ◽  
pp. 61
Author(s):  
Aditya Setyawan R ◽  
Mustika Hadijati ◽  
Ni Wayan Switrayni
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Linear Regression ◽  
Statistical Method ◽  
Estimation Method ◽  
Least Square ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Regression Parameters ◽  
Independent Variable

Regression analysis is one statistical method that allows users to analyze the influence of one or more independent variables (X) on a dependent variable (Y).The most commonly used method for estimating linear regression parameters is Ordinary Least Square (OLS). But in reality, there is often a problem with heteroscedasticity, namely the variance of the error is not constant or variable for all values of the independent variable X. This results in the OLS method being less effective. To overcome this, a parameter estimation method can be used by adding weight to each parameter, namely the Generalized Least Square (GLS) method. This study aims to examine the use of the GLS method in overcoming heteroscedasticity in regression analysis and examine the comparison of estimation results using the OLS method with the GLS method in the case of heteroscedasticity.The results show that the GLS method was able to maintain the nature of the estimator that is not biased and consistent and able to overcome the problem of heteroscedasticity, so that the GLS method is more effective than the OLS method.

scholarly journals Factors Affecting the Amount of Investment Loans in Commercial Banks with the Application of Linear Regression Analysis Methods

2019 ◽  
Vol 20 (1) ◽  
pp. 48-54
Author(s):  
Khafsah Joebaedi ◽  
D Susanti ◽  
N Warwah ◽  
K Parmikanti ◽  
Badrulfalah Badrulfalah
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Interest Rates ◽  
Commercial Banks ◽  
Capita Income ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Least Square ◽  
Independent Variables ◽  
Inflation Rates

Several factors can affect the amount of investment credit issued by the bank. This study discusses the effect of interest rates, inflation rates, capita income, and the number of medium-large industries on the number of investment loans, using the method of linear regression analysis. The independent variables examined to determine the influence of each and all the independent variables on the number of investment loans in commercial banks, namely interest rates, inflation rates, capita income, and the number of medium-large industries using the parameter estimation method, OLS (Ordinary Least Square).

scholarly journals PENERAPAN METODE GENERALIZED RIDGE REGRESSION DALAM MENGATASI MASALAH MULTIKOLINEARITAS

2013 ◽  
Vol 2 (1) ◽  
pp. 54
Author(s):  
NI KETUT TRI UTAMI ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Ridge Regression ◽  
Estimation Method ◽  
Minimum Variance ◽  
Least Square ◽  
Ordinary Least Square ◽  
Parameter Estimation Method ◽  
Alternative Method ◽  
Highly Correlated

Ordinary least square is parameter estimation method for linier regression analysis by minimizing residual sum of square. In the presence of multicollinearity, estimators which are unbiased and have a minimum variance can not be generated. Multicollinearity refers to a situation where regressor variables are highly correlated. Generalized Ridge Regression is an alternative method to deal with multicollinearity problem. In Generalized Ridge Regression, different biasing parameters for each regressor variables were added to the least square equation after transform the data to the space of orthogonal regressors. The analysis showed that Generalized Ridge Regression was satisfactory to overcome multicollinearity.

scholarly journals PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI

2014 ◽  
Vol 3 (4) ◽  
pp. 130
Author(s):  
NI MADE METTA ASTARI ◽  
NI LUH PUTU SUCIPTAWATI ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Regression Analysis ◽  
Statistical Analysis ◽  
Confidence Intervals ◽  
Bootstrap Method ◽  
Least Square ◽  
Prediction Tool ◽  
Analysis Method ◽  
Ordinary Least Square ◽  
Independent Variable ◽  
Parameter Estimators

Statistical analysis which aims to analyze a linear relationship between the independent variable and the dependent variable is known as regression analysis. To estimate parameters in a regression analysis method commonly used is the Ordinary Least Square (OLS). But the assumption is often violated in the OLS, the assumption of normality due to one outlier. As a result of the presence of outliers is parameter estimators produced by the OLS will be biased. Bootstrap Residual is a bootstrap method that is applied to the residual resampling process. The results showed that the residual bootstrap method is only able to overcome the bias on the number of outliers 5% with 99% confidence intervals. The resulting parameters estimators approach the residual bootstrap values ??OLS initial allegations were also able to show that the bootstrap is an accurate prediction tool.

scholarly journals PENGARUH KOMPETENSI DAN KOMPENSASI TERHADAP KINERJA KARYAWAN

2020 ◽  
Vol 3 (1) ◽  
pp. 58-67
Author(s):  
Yadi Maryadi ◽  
Yulia Misrania
Keyword(s):  
Regression Coefficient ◽  
Employee Performance ◽  
Regression Equation ◽  
Least Square ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Hotel Employees ◽  
Independent Variable ◽  
The City

This study aims to determine the effect of Competence and Compensation on Employee Performance (Case Study on Hotel Employees in the city of Pagar Alam). By using primary data and secondary data, this research uses the Ordinary Least Square method. The results of this study are the findings of the influence of Compensation and Compensation on Employee Performance (Case Study on Hotel Employees in the City of Pagar Alam) can be explained as follows. Based on the Regression analysis in the table above, the constant value α = 38,868 and coefficient b1 =. 083 and b2 = .396, so that the multiple linear regression equation is: Y = 38.868+ 0.083X1 + 0.396X2. The regression equation that has been obtained can be used to predict the value of the independent variable and the dependent variable, as follows: A constant value of 38,868 means that if all independent variables are zero, then the performance of hotel clerks in the city of Pagar Alam is valued at 38,868. Competence (X1) has a regression coefficient of .083 meaning that each increase in Competency by 1 unit then the performance of hotel employees increases by .083 assuming the other factors remain. Compensation (X2) has a regression coefficient of .396 meaning that each increase in compensation is 1 unit, the performance of hotel employees increases by .396. From the determination coefficient table above, it can be seen that the R Square figure is 0.931. This means that the influence between the independent variables with the dependent variable is 93.1% while the remaining 6.9% is influenced by other factors outside the regression model analyzed. From these figures it can be concluded that the influence of the independent variable with the dependent variable is very strong.   Penelitian ini bertujuan untuk mengetahui pengaruh Kompetensi dan Kompensasi terhadap Kinerja Karyawan (Studi Kasus pada Karyawan hotel di Kota Pagar Alam). Dengan menggunakan data primer dan data skunder penelitian ini menggunakan metode Regresi Linear Sederhana (Ordinary Least Square). Adapun hasil Penelitian ini adalah Hasil temuan mengenai pengaruh Komptensi dan Kompensasi terhadap Kinerja Karyawan (Studi Kasus pada Karyawan hotel di Kota Pagar Alam) dapat dijelaskan sebagai berikut. Berdasarkan analisis Regresi pada tabel diatas didapatkan  nilai konstanta α = 38.868 dan koefisien b1 = . 083  dan b2 = .396, sehingga persamaan regresi linier berganda adalah :  Y =  38.868+ 0.083X1 + 0.396X2. Persamaan regresi yang telah diperoleh dapat dipergunakan untuk memprediksi nilai variabel independen dan variabel dependen yaitu sebagai berikut : Nilai konstanta sebesar 38.868 artinya bahwa jika semua variabel independen benilai nol, maka kinerja kayawan hotel di kota Pagar Alam bernilai sebesar 38.868. Kompetensi (X1) mempunyai koefisien regresi sebesar .083 artinya setiap kenaikan Kompetensi sebesar 1 satuan maka kinerja karyawan hotel naik sebesar .083 dengan asumsi faktor-faktor yang lain tetap. Kompensasi (X2) mempunyai koefisien regresi sebesar .396 artinya setiap kenaikan kompensasi sebesar 1 satuan maka kinerja karyawan hotel naik sebesar .396. Dari tabel koefisien determinasi di atas, dapat dilihat bahwa angka R Square sebesar 0.931. Hal ini berarti pengaruh antar variabel independen dengan variabel dependen sebesar 93,1% sedangkan sisanya sebesar 6,9% dipengaruhi oleh faktor-faktor lain di luar model regresi yang dianalisis. Dari angka tersebut dapat diambil kesimpulan bahwa pengaruh antara variabel independen dengan variabel dependen sangat kuat.

scholarly journals Penanganan Endogenitas Modal Sosial Pada Pemodelan Kemiskinan Rumah Tangga di Indonesia Dengan Metode Two Probit Least Square (2PLS)

2019 ◽  
Vol 8 (1) ◽  
pp. 19-26
Author(s):  
Eko Yulian
Keyword(s):  
Social Capital ◽  
Human Capital ◽  
Regression Analysis ◽  
Poverty Alleviation ◽  
Least Square ◽  
Probit Regression ◽  
Variable Size ◽  
Factors Affecting ◽  
Independent Variables ◽  
Independent Variable

Kemiskinan merupakan salah satu permasalahan mendasar yang telah menjadi perhatian utama berbagai negara di dunia termasuk Indonesia. Agar pengentasan kemiskinan berjalan efektif tentunya perlu diketahui dan diukur kuat pengaruh faktor-faktor yang mempengaruhi kemiskinan. Faktor-faktor tersebut diantaranya adalah modal SDM dan modal sosial. Untuk mengukur kontribusi faktor-faktor tersebut digunakan analisis regresi. Variabel bebas pada penelitian ini bersifat kategorik biner (1=miskin, 0=tidak) sehingga analisis regresi yang bisa digunakan adalah analisis regresi logistik dan probit. Penelitian ini menggunakan regresi probit untuk mengetahui pengaruh modal sosial dan modal SDM terhadap kemiskinan. Pada analisis regresi probit, asumsi yang harus dipenuhi adalah tidak adanya korelasi antara variabel bebas dan error pada model, apabila hal tersebut tidak terpenuhi maka akan muncul permasalahan yang disebut dengan endogenitas yang mengakibatkan hasil taksiran parameter yang dihasilkan bias. Pada penelitian ini diperoleh informasi bahwa variabel modal sosial merupakan variabel endogen sehingga digunakan metode Two Probit Least Square (2PLS) untuk mengatasi permasalahan endogenitas yang terjadi. Berdasarkan hasil regresi probit dengan menggunakan metode 2PLS diperoleh tiga variabel bebas yang berpengaruh negatif terhadap peluang rumah tangga menjadi miskin, variabel-variabel tersebut adalah modal sosial, lama sekolah dan umur. Sedangkan variabel ukuran rumah tangga berpengaruh positif terhadap peluang rumah tangga menjadi miskin di Indonesia. [Poverty is one of the fundamental problems. It has been a major concern of governments in various countries around the world, including Indonesia. In order for poverty alleviation to be effective, it is necessary to know and measured strongly the influence of factors affecting poverty. These factors include human capital and social capital. To measure the contribution of these factors is used regression analysis. The independent variables in this study are binary categorical (1=poor, 0=no) so that regression analysis that can be used is logistic and probit regression analysis. This research uses probit regression to know the influence of social capital and human capital to poverty. In the probit regression analysis, the assumption that must be met is the absence of correlation between the independent variable and error in the model, if it is not fulfilled then the problem will arise called endogeneity which can lead to bias parameter. In this study obtained information that social capital variable is an endogenous variable, so Two Probit Least Square (2PLS) method used to overcome the problem of endogeneity that happened. Based on the results of probit regression using 2PLS method obtained three independent variables that negatively affect the probability of households to be poor, these variables are social capital, school, and age. While the variable size of households positively affects the probability of households being poor in Indonesia.]

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.

scholarly journals Pendugaan Produktivitas Bagan Perahu dengan Regresi Gulud, LASSO dan Elastic-net

2018 ◽  
Vol 2 (2) ◽  
pp. 7-14
Author(s):  
Resty Fanny ◽  
Anik Djuraidah ◽  
Aam Alamudi
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Ridge Regression ◽  
Penalized Regression ◽  
Statistical Technique ◽  
Elastic Net ◽  
Least Square ◽  
Productivity Change ◽  
Independent Variables ◽  
Independent Variable

Regression analysis is a statistical technique to examine and model the relationship between dependent variable and independent variable. Multiple linear regression includes more than one independent variable. Multicollinearity in multiple linear regression occurs when the independent variables has correlations. Multicolinearity causes the estimator by ordinary least square to be unstable and produce a large variety. Multicollinearity can be overcome by the addition of penalized regression coefficient. The purpose of this research is modeling ridge regression, LASSO, and elastic-net. Data which is data of fisherman catch at Carocok Beach of Tarusan Sumatera Barat as dependent variable and amount of labor, amount of fuel, volume of fishing/waring boat, number of catches, ship size, number of boat wattage, sea experience, education and age of fisher as independent variables. The best model provided by LASSO that has a RMSEP value of validated regression model is minimum than ridge regression and elastic-net. LASSO shrinked amount of labor, amount of fuel and number of wattage equal zero. There can be influence (productivity change) that is volume of fishing/waring boat and boat size that used by fisher.

scholarly journals ANALISIS REGRESI KOMPONEN UTAMA UNTUK MENGATASI MASALAH MULTIKOLINIERITAS DALAM ANALISIS REGRESI LINIER BERGANDA

2012 ◽  
Vol 6 (1) ◽  
pp. 31-40
Author(s):  
Gresyea L. Marcus ◽  
Henry J. Wattimanela ◽  
Yopi A. Lesnussa
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Least Square Method ◽  
Component Analysis ◽  
Least Square ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Analysis Technique ◽  
Independent Variable ◽  
Very High

The climate in Ambon, are influenced by sea climate and season climate, cause of this island arrounded by sea, it is make very high rainfall intensity. A very high collinearity between independent variables, make the estimate can not rely be ordinary least square method so it market with not real regretion coefficient and the collinearity. Collinearity can be detected by linier correlation coefficient between independent variables and also with VIF way. Regretion principal component analysis is used to remove collinearity and all of independent variable into model, this analysis is regretion analysis technique wher eare combinated with principal component analysis technique. The object of this analysis is to simplify the variable by overcast it dimension, we can do it removes the correlation between coefficient by transformation. Regresion can help to solve this case rainfall in Ambon on 2010. So the colinearity to independent variables can be overcome and then we can get the best regretion rutes.

scholarly journals PERBANDINGAN TRANSFORMASI BOX-COX DAN REGRESI KUANTIL MEDIAN DALAM MENGATASI HETEROSKEDASTISITAS

2015 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
NI WAYAN YUNI CAHYANI ◽  
I GUSTI AYU MADE SRINADI ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Quantile Regression ◽  
Linear Regression Analysis ◽  
Least Square ◽  
Power Transformation ◽  
Ordinary Least Square

Ordinary least square (OLS) is a method that can be used to estimate the parameter in linear regression analysis. There are some assumption which should be satisfied on OLS, one of this assumption is homoscedasticity, that is the variance of error is constant. If variance of the error is unequal that so-called heteroscedasticity. The presence heteroscedasticity can cause estimation with OLS becomes inefficient. Therefore, heteroscedasticity shall be overcome. There are some method that can used to overcome heteroscedasticity, two among those are Box-Cox power transformation and median quantile regression. This research compared Box-Cox power transformation and median quantile regression to overcome heteroscedasticity. Applied Box-Cox power transformation on OLS result ????2point are greater, smaller RMSE point and confidencen interval more narrow, therefore can be concluded that applied of Box-Cox power transformation on OLS better of median quantile regression to overcome heteroscedasticity.

scholarly journals ANALISIS DETERMINAN PENDAPATAN PEDAGANG PASAR BANJARSARI KOTA PEKALONGAN PASCA KEBAKARAN

2020 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Nur Elitami Insan ◽  
Arif Pujiyono
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Working Hours ◽  
Multiple Linear Regression Analysis ◽  
Least Square ◽  
Ordinary Least Square ◽  
Business Location ◽  
Dominant Influence

The income of traders in the Banjarsari Emergency Market Pekalongan is influenced by various factors, one of which is the trader resources (capital, working hours, business length, education, and business location). This study aims to determine the effect of capital, working hours, business length, education, and business location on the income of traders in the Banjarsari Emergency Market in Pekalongan. There as 95 traders of Banjarsari Emergency Market in Pekalongan were taken as samples with purposive sampling and accidental sampling. This study uses multiple linear regression analysis tools with the Ordinary Least Square (OLS) method. The results showed that capital, length of business, and location of business affect the income of traders. While working hours and education have not affected the income of the trader. Capital provides the most dominant influence on income trader

Sign in / Sign up

Export Citation Format

Share Document