Month: April 2023
How to Test Heteroscedasticity in Linear Regression and Interpretation in R (Part 3)
One of the assumptions required in Ordinary Least Squares (OLS) linear regression is that the variance of the residuals is constant. This assumption is often referred to as the homoscedasticity assumption. Some researchers are more familiar with the term heteroscedasticity test.
How to Analyze Multicollinearity in Linear Regression and its Interpretation in R (Part 2)
Non-multicollinearity is one of the assumptions required in the ordinary least square (OLS) method of linear regression analysis. Non-multicollinearity assumption implies that there is no strong correlation among the independent variables in the equation.
How to Analyze Multiple Linear Regression and Interpretation in R (Part 1)
Multiple linear regression analysis has been widely used by researchers to analyze the influence of independent variables on dependent variables. There are many tools that researchers can use to analyze multiple linear regression.
How to Transform Natural Logarithm (ln) and Reverse (anti-Ln) in Excel
Researchers often transform data to convert original data into another form to meet certain assumptions. Researchers can do several forms of data transformation. Natural logarithm transformation is a form of transformation frequently used by researchers in data analysis.
Step-by-Step Tutorial: Finding Predicted and Residual Values in Linear Regression with Excel
In linear regression analysis, residual values play an important role in supporting the main analysis. Residual values are the difference between actual values and predicted values. In the assumption testing of linear regression using the OLS method, residual values are needed for the testing of assumptions.