Given Z~N(0,1), Z^2 follows
χ_1^2 Chi-square Probability Distribution with one degree of freedom
Given Z_i~N(0,1), ∑_(i=1)^ν▒Z_i^2 follows
χ_ν^2 Chi-square Probability Distribution with ν degree of freedom
Given E_ij=n×p_ij=(r_i×c_j)/n, U=∑_(∀i,j)▒(O_ij-E_ij )^2/E_ij follows
χ_((r-1)(c-1))^2 Chi-square Probability Distribution with ν=(r-1)(c-1) degree of freedom
Given E_i=n×p_i, U=∑_(i=1)^m▒(O_i-E_j )^2/E_i follows
χ_(m-1)^2 Chi-square Probability Distribution with ν=m-1 degree of freedom
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The Chi-square probability distribution is a probability distribution that describes the distribution of the sum of squared standard normal random variables. It is often used in hypothesis testing and is characterized by its degrees of freedom. The shape of the distribution depends on the degrees of freedom parameter, with larger degrees of freedom resulting in a more symmetric and bell-shaped distribution.
No. Normal distribution is a continuous probability.
Yes. When we refer to the normal distribution, we are referring to a probability distribution. When we specify the equation of a continuous distribution, such as the normal distribution, we refer to the equation as a probability density function.
The statement is true that a sampling distribution is a probability distribution for a statistic.
how do i find the median of a continuous probability distribution
None. The full name is the Probability Distribution Function (pdf).