When conducting linear regression analysis on your research data, you naturally hope that some independent variables significantly affect the dependent variable. Achieving this indicates that you’ve successfully selected independent variables that are presumed to influence the dependent variable.
Residual values in linear regression analysis need to be calculated for several purposes. In linear regression using the ordinary least squares method, one of the assumptions that must be met is that residuals must be normally distributed, hence the necessity to first calculate residual values. However, before calculating the residual values, we need to first …
Calculating Predicted Y and Residual Values in Simple Linear Regression Read More »
Testing for normality in linear regression analysis is a crucial part of inferential method assumptions, requiring regression residuals to be normally distributed. Residuals are the differences between observed values and those predicted by the linear regression model.
