2371 NA The log likelihood test statistics have a chi-square distribution
with one degree of freedom.
For some distributions, like t distribution or Chi-square distribution
, the t or Chi-square value depends on a single value of degree of freedom only.
t] is large enough, it is possible to use the Central Limit Theorem to approximate the Chi-square distribution
to a Gaussian distribution , and the following approximation holds
dx,[lambda]] is a left-tail non-central Chi-square distribution
with degree of freedom (df) and non-centrality parameter [lambda] = [Nw.
1-[alpha],df] the [alpha]th percentile of the chi-square distribution
with df, degrees of freedom, n is the sample size, df = n - m (number of independent random samples) is degrees of freedom defined as the number of values that are free to vary, and Z(1-P)/2 is the pth percentile of the standard normal distribution.
The chi-square distribution
test and largest normalized residual tests are used to detect and identify the malicious data .
The distinct advantage of the prescribed methods is that it circumvents the uncertainty of sample variance by taking account of the underlying chi-square distribution
of sample variance and permits a corrected sample size determination according to the desired assurance probability and expected power considerations.
The calculated statistics is compared with the critical value of the chi-square distribution
Results indicate a noncentral chi-square distribution
for rows and columns of the GxE interaction matrix, which was also verified by the Kolmogorov-Smirnov test and Q-Q plot.
An assumption is also made when using the chi-square distribution
as an approximation to the distribution of kh2, is that the frequencies expected under independence should not be "too small".
2] has an asymptotic chi-square distribution
with (C-1) (S-1) degrees of freedom.
The square of a normal N(0, 1) variable has the chi-square distribution