Category: Assumptions of Linear Regression
How to Test Linearity Assumption in Linear Regression using Scatter Plot
The linearity test is one of the assumption tests in linear regression using the ordinary least square (OLS) method. The objective of the linearity test is to determine whether the distribution of the data of the dependent variable and the independent variable forms a linear line pattern or not?
Multicollinearity Test and Interpreting the Output in Linear Regression
One of the assumptions in linear regression using the ordinary least square (OLS) method is that there is no strong correlation between independent variables. To get the Best Linear Unbiased Estimator in linear regression with ≥ 2 independent variables, you must be fulfilled the non-multicollinearity assumption.
Heteroscedasticity Test and How to Interpret the Output in Linear Regression
The objective of the heteroscedasticity test is to determine whether the variance of residuals is constant. One of the assumption tests in linear regression using the ordinary least square (OLS) method is that the variance of residuals is constant.
How to Test the Normality Assumption in Linear Regression and Interpreting the Output
The normality test is one of the assumption tests in linear regression using the ordinary least square (OLS) method. The normality test is intended to determine whether the residuals are normally distributed or not.
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.
How to Test the Multicollinearity in Multiple Linear Regression
When choosing multiple linear regression analysis, we include at least two independent variables into the model. To obtain the best linear unbiased estimator, we must test the assumptions. One of the assumptions that need to be tested is the multicollinearity test.
Autocorrelation Test on Time Series Data using Linear Regression
The autocorrelation test is one of the assumptions of linear regression with the OLS method. On this occasion, I will discuss the autocorrelation test on time series data. Before discussing the autocorrelation test, you need to know first that the autocorrelation test was conducted on time series, not cross-sectional data.
Regression Assumption Test: How and Why to Do?
Incidentally, the topic that I will discuss this time is the linear regression assumption test using the ordinary least square method. “Why do we have to use the linear regression assumption test? Can I directly do a regression analysis?” To answer this question, you need to go back a little bit by turning page after page from a book on econometric theory or socio-economic statistics, okay? One of the main points that you need to pay attention to is that you are doing an estimate when you analyze research data and then choose regression as an analysis tool.