Tag: regression model assumptions
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.