A) The question would be "Does cell phone use time correlate to brain cancer?". The population is all cell phone users. The sample is the 469 people with brain cancer, and the 422 healthy people.
B) This sample size may not portray the entire population of users because the positive results may have been fabricated by factors other than cell phone use. You would have to figure out the population size and determine the correct sample size.
C) This study may not portray cell phone use correlation over a large population efficiently because of the sample size and the predetermined conclusion that cell phone use causes cancer.
Answer:
1295/36
Step-by-step explanation:
the statement tell us:
(7-(5/6))*(7-(7/6))
we have:
(7-(5/6))=((6*7)-5)/6=(42-5)/6=37/6
and we have:
(7-(7/6))=((6*7)-7)/6=(42-7)/6=35/6
we need multiply both terms:
(37/6)*(35/6)=(37*35)/(6*6)
finally we have
1295/36
Answer:
The answer is 5 over 7
Step-by-step explanation:
Answer:
a) 
And we can use the probability mass function and we got:
And adding we got:

b)
c) ![P(X>3) = 1-P(X \leq 3) = 1- [P(X=0)+P(X=1)+P(X=2)+P(X=3)]](https://tex.z-dn.net/?f=P%28X%3E3%29%20%3D%201-P%28X%20%5Cleq%203%29%20%3D%201-%20%5BP%28X%3D0%29%2BP%28X%3D1%29%2BP%28X%3D2%29%2BP%28X%3D3%29%5D%20)


And replacing we got:
![P(X>3) = 1-[0.0115+0.0576+0.1369+0.2054]= 1-0.4114= 0.5886](https://tex.z-dn.net/?f=%20P%28X%3E3%29%20%3D%201-%5B0.0115%2B0.0576%2B0.1369%2B0.2054%5D%3D%201-0.4114%3D%200.5886)
d) 
Step-by-step explanation:
Previous concepts
The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".
Solution to the problem
Let X the random variable of interest, on this case we now that:
The probability mass function for the Binomial distribution is given as:
Where (nCx) means combinatory and it's given by this formula:
Part a
We want this probability:

And we can use the probability mass function and we got:
And adding we got:

Part b
We want this probability:

And using the probability mass function we got:
Part c
We want this probability:

We can use the complement rule and we got:
![P(X>3) = 1-P(X \leq 3) = 1- [P(X=0)+P(X=1)+P(X=2)+P(X=3)]](https://tex.z-dn.net/?f=P%28X%3E3%29%20%3D%201-P%28X%20%5Cleq%203%29%20%3D%201-%20%5BP%28X%3D0%29%2BP%28X%3D1%29%2BP%28X%3D2%29%2BP%28X%3D3%29%5D%20)


And replacing we got:
![P(X>3) = 1-[0.0115+0.0576+0.1369+0.2054]= 1-0.4114= 0.5886](https://tex.z-dn.net/?f=%20P%28X%3E3%29%20%3D%201-%5B0.0115%2B0.0576%2B0.1369%2B0.2054%5D%3D%201-0.4114%3D%200.5886)
Part d
The expected value is given by:

And replacing we got:
