The author analyzes mammal sleep with 62 species in 1976 by using Lasso method (least absolute shrinkage and selection operator) that provides stability, higher selection variables, computational efficiency, and higher prediction accuracy. the results of Average Parameter Estimate for using adaptive Lasso in SAS indicates that the position of slow wave and paradoxical sleep is account for 100%, overall danger index is 93%. The distributions of overall danger index and slow wave with paradoxical sleep as wee as gestation time from Refit model shows normal histogram for paradoxical sleep. In partition statement of “glmselect” procedure, ASE value (Average Square Error) of the validation from overall danger index is the minimum of all parameters in the selected model. On the other hand, in selection steps for ASE, the adaptive Lasso method seems to have fewer than Lasso; for complicate and large data, elastic net can deal with more parameters than observations and combine one and a couple of groups that are consist of multiple variables by shrinking the coefficients of correlated variables toward each other.