Tag: variance inflation factor
How to Calculate Tolerance Value and Variance Inflation Factor (VIF)
The tolerance value and Variance Inflation Factor (VIF) are important metrics that you can use to detect multicollinearity among independent variables. If we recall the basic theory, multicollinearity testing is an assumption test in the Ordinary Least Squares (OLS) regression method, which aims to ensure that there is no strong correlation between independent variables.
How to Calculate the Variance Inflation Factor (VIF) in a Multicollinearity Test for Regression
In linear regression analysis, to obtain the best linear unbiased estimator, you need to perform a series of assumption tests. One of the assumption tests required in linear regression is the multicollinearity test.
Differences in Assumptions of Normality, Heteroscedasticity, and Multicollinearity in Linear Regression Analysis
If you analyze research data using linear regression, it is crucial to understand the required assumptions. Understanding these assumption tests is essential to ensure consistent and unbiased analysis results.
How to Analyze Multicollinearity in Linear Regression Using R Studio
In linear regression analysis using the Ordinary Least Square method, it is necessary to ensure that there is no strong correlation between independent variables. To obtain the best linear unbiased estimator, there must not be a strong correlation between the independent variables.
Multicollinearity Test in Multiple Linear Regression Analysis
In multiple linear regression analysis, there is an assumption that the model constructed is not affected by multicollinearity issues, where two or more independent variables are strongly correlated. Multicollinearity can lead to errors in parameter estimation and reduce the reliability of the model.
Multicollinearity Test using Variance Inflation Factor (VIF) in SPSS
Multicollinearity detection is one of the assumption tests that must be performed on multiple linear regression. This assumption test was conducted to obtain the best linear unbiased estimator.
Non-Multicollinearity Test in Multiple Linear Regression
When analyzing data using linear regression using the Ordinary Least Square (OLS) method, it takes an understanding of the assumption test that must be passed. The non-multicollinearity test is necessary to get the best linear unbiased estimator. The multiple linear regression OLS method has been widely applied in various fields: economics, agribusiness, and socio-economic fields. The estimation of the output of this linear regression has many benefits. Various research problems can be solved with this analytical approach. When we choose to use regression analysis, we are trying to see the influence or impact of one or more variables on other variables. Therefore, many researchers, lecturers, students, and practitioners choose linear regression using the OLS method as a data analysis tool.