nonparametric skew造句

例句与造句

1. In contrast the nonparametric skew is-0.110.
2. The nonparametric skew does not satisfy these axioms.
3. This relationship holds for all the Pearson distributions and all of these distributions have a positive nonparametric skew.
4. It has been suggested that random variates from distributions with a positive nonparametric skew will obey this law.
5. These conditions have since been generalised Any distribution for which this holds has either a zero or a positive nonparametric skew.
6. It's difficult to find nonparametric skew in a sentence. 用nonparametric skew造句挺难的
7. The nonparametric skew is one third of the Pearson 2 skewness coefficient and lies between & minus; 1 and + 1 for any distribution.
8. For example, in a skewed distribution, the nonparametric skew ( and Pearson's skewness coefficients ) measure the bias of the median as an estimator of the mean.
9. Analyses have been made of some of the relationships between the mean, median, mode and standard deviation . and these relationships place some restrictions of the sign and magnitude of the nonparametric skew.
10. In the older notion of nonparametric skew, defined as ( \ mu-\ nu ) / \ sigma, where " ?" is the mean, " ? " is the median, and " ? " is the standard deviation, the skewness is defined in terms of this relationship : positive / right nonparametric skew means the mean is greater than ( to the right of ) the median, while negative / left nonparametric skew means the mean is less than ( to the left of ) the median.
11. In the older notion of nonparametric skew, defined as ( \ mu-\ nu ) / \ sigma, where " ?" is the mean, " ? " is the median, and " ? " is the standard deviation, the skewness is defined in terms of this relationship : positive / right nonparametric skew means the mean is greater than ( to the right of ) the median, while negative / left nonparametric skew means the mean is less than ( to the left of ) the median.
12. In the older notion of nonparametric skew, defined as ( \ mu-\ nu ) / \ sigma, where " ?" is the mean, " ? " is the median, and " ? " is the standard deviation, the skewness is defined in terms of this relationship : positive / right nonparametric skew means the mean is greater than ( to the right of ) the median, while negative / left nonparametric skew means the mean is less than ( to the left of ) the median.