Each Chi-square random variable is associated with a degree of freedom (υ),
.
As υ increase, Chi-square curves become more symmetric.
Z2, the square of a normal[0,1] random variable, follows a
distribution.
The sum of 2 independent Chi-square random variables with
υ
1
, υ
2
degrees of freedom respectively, has a Chi-square distribution with
υ = υ
1
+ υ
2
degrees of freedom.
E(
) = υ
and V(
) = 2 υ.
If {X
1
, X
2
, …, Xn} is a random sample of size n drawn from normal population with mean μ and standard deviation σ (i.e., X ~ normal[μ,σ]), then {(n-1)S2 }/ σ2 =
~
.
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Chi-square is a statistic used to assess the degree of the relationship and degree of association between two nominal variables
The characteristics of the chi-square distribution are: A. The value of chi-square is never negative. B. The chi-square distribution is positively skewed. C. There is a family of chi-square distributions.
It is not negative. it is positively skewed, and it approaches a normal distribution as the degrees of freedom increase. Its shape is NEVER based on the sample size.
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.
There are different methods for comparing the mean, variance or standard error, distribution or other characteristics of populations. Without more specific information it is not possible to answer the question.