Answer:
Approximately normal for large sample sizes
Step-by-step explanation:
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean 
and standard deviation 
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean and standard deviation 
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
In this question:
The distribution is unknown, so the sampling distribution will only be approximately normal when n is at least 30.
So the correct answer should be:
Approximately normal for large sample sizes
The answer
f(x) = 0.7(6)x = <span>f(x) = 0.7(6)^x, and </span><span>g(x) = 0.7(6)–x= </span>g(x) = 0.7(6)^-x=1/<span>0.7(6)^x
so </span>
g(x) =1/<span>0.7(6)^x=1 /</span><span><span>f(x)
</span> the relationship between f and g are </span>g(x) =1 /<span>f(x) or </span><span>g(x) . <span>f(x) = 1</span> </span>
Total number of students surveyed = 200
Number of male students = 80
Number of female students = 200 - 80 = 120
Number of brown eyed male students = 60
Probability of a brown eyed male student = 60 / 80 = 0.75.
Since, <span>eye color and gender are independent, this means that eye color is not affected by the gender. Thus, we expect a similar probability of brown eye for female as we had for male.
Let the number expected of brown eyed females be x, then x / 120 = 0.75.
Thus, x = 120(0.75) = 90.
Therefore, the number female students surveyed expected to be brown eyed is 90.</span>
