Thank you for reading CFI’s guide to Sum of Squares. The relationship between the three types of sum of squares can be summarized by the following equation: The residual sum of squares can be found using the formula below: Generally, a lower residual sum of squares indicates that the regression model can better explain the data, while a higher residual sum of squares indicates that the model poorly explains the data. In other words, it depicts how the variation in the dependent variable in a regression model cannot be explained by the model. The residual sum of squares essentially measures the variation of modeling errors. While it is possible to calculate linear regression by hand, it involves a lot of sums and squares, not to mention sums of. Residual sum of squares (also known as the sum of squared errors of prediction)
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |