In other words, the standard error of the estimate can be used to determine a range within which the dependent Y variable can be predicted with varying degrees of statistical confidence based on the regression coefficients and values for the X variables. The standard error of the estimate provides a very useful means for estimating confidence intervals around any particular ˆYt estimate, given values for the independent X variables. If there is a great deal of scatter about the regression line, then ˆYt often differs greatly from each Yt, and the standard error of the estimate will be large. No scatter about the regression line exists when the standard error of the estimate equals zero. If each data point were to lie exactly on the regression line, then the standard error of the estimate would equal zero since each ˆYt would exactly equal Yt. The standard error of the estimate increases with the amount of scatter about the sample regression line. A useful measure for examining the accuracy of any regression model is the standard error of the estimate, SEE, or the standard deviation of the dependent Y variable after controlling for the influence of all X variables.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |