例句與造句

1. For non-zero sample skewness one needs to solve a system of two coupled equations.
2. However it is possible that the sample skewness is larger, and then \ alpha cannot be determined from these equations.
3. Another alternative is to calculate the support interval range ( \ hat { c }-\ hat { a } ) based on the sample variance and the sample skewness.
4. D'Agostino's K-squared test is a goodness-of-fit normality test based on a combination of the sample skewness and sample kurtosis, as is the Jarque Bera test for normality.
5. See section titled " Kurtosis bounded by the square of the skewness " for a numerical example and further comments about this rear edge boundary line ( sample excess kurtosis-( 3 / 2 ) ( sample skewness ) 2 = 0 ).
6. It's difficult to find sample skewness in a sentence. 用sample skewness造句挺難的
7. Alternatively, rules of thumb based on the sample skewness and kurtosis have also been proposed, such as having skewness in the range of & minus; 0.8 to 0.8 and kurtosis in the range of & minus; 3.0 to 3.0.
8. It is for this reason that we have spelled out " sample skewness ", etc ., in the above formulas, to make it explicit that the user should choose the best estimator according to the problem at hand, as the best estimator for skewness and kurtosis depends on the amount of skewness ( as shown by Joanes and Gill ).
9. Since the skewness and the excess kurtosis are independent of the parameters \ hat { a }, \ hat { c }, the parameters \ hat { \ alpha }, \ hat { \ beta } can be uniquely determined from the sample skewness and the sample excess kurtosis, by solving the coupled equations with two known variables ( sample skewness and sample excess kurtosis ) and two unknowns ( the shape parameters ):
10. Since the skewness and the excess kurtosis are independent of the parameters \ hat { a }, \ hat { c }, the parameters \ hat { \ alpha }, \ hat { \ beta } can be uniquely determined from the sample skewness and the sample excess kurtosis, by solving the coupled equations with two known variables ( sample skewness and sample excess kurtosis ) and two unknowns ( the shape parameters ):
11. As remarked, for example, by Bowman and Shenton, sampling in the neighborhood of the line ( sample excess kurtosis-( 3 / 2 ) ( sample skewness ) 2 = 0 ) ( the just-J-shaped portion of the rear edge where blue meets beige ), " is dangerously near to chaos ", because at that line the denominator of the expression above for the estimate ? = ? + ? becomes zero and hence ? approaches infinity as that line is approached.