The number of degrees of freedom associated with the chi-square distribution in a test of independence is a. number of sample items minus 1. b. number of populations minus number of estimated parameters minus 1. c. number of populations minus 1. d. number of rows minus 1 times number of columns minus 1.

d) the number of rows minus 1 times number of columns minus 1

The degrees of freedom γ= (r-1)(s-1)

Step-by-step explanation:

chi-square distribution:-

In this chi-square test , we test if two attributes A  and B under consideration are independent or not .

here we will choose null hypothesis (H₀): Attributes are independent

Degrees of freedom :  γ= (r-1)(s-1)

where r = number of rows

and 's' = number of columns

chi - squared test

Χ² = ∑(O-E)²/ E

'O' be the observed frequency

'E' be the Expected frequency

the answer   is 200

step-by-step explanation:

this is a problem about poisson probabilities.   i don't know if you've

already studied poisson distributions, or if you have a place to look

to read more about the subject.  

here's how you can see a poisson distribution coming.   when there are

a lot of events that may happen at any time (or any place), and when

you're counting how many of them happen at a particular time (or a

particular place), and each event is independent of the others, then

the result is a poisson distribution.   this is roughly true for the

chocolate chips; one chip being in a cookie has only a small effect on

the probability that another chip will be in the cookie.

once you know you have a poisson distribution, you know a lot.   there

is only one parameter to a poisson distribution, so if you know the

mean, you know everything.   the form of the distribution is

  probability of n chips = {[e^(-x)]*(x^n)} / (n! )

24+x=y

step-by-step explanation: