Simple linear regression analysis is a useful statistical technique for measuring and understanding the relationship between two variables. In this analysis, one variable (independent variable) is used to predict or explain the other variable (dependent variable).
Residual and predicted Y have an important role in linear regression analysis using the Ordinary Least Square (OLS) method. Several OLS linear regression method assumptions were conducted to test the residuals, including normally distributed residuals and constant residual variance.
The residual value in linear regression analysis needs to be calculated first before calculating the variance. In addition, the linear regression of the ordinary least square method must pass the assumption test that the residuals must be normally distributed. However, before calculating the residual value, you must first calculate the predicted Y value. Therefore, we …
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