The answer to number 18 is Cassie
Answer:
A normal model is a good fit for the sampling distribution.
Step-by-step explanation:
According to the Central limit theorem, if from an unknown population large samples of sizes n > 30, are selected and the sample proportion for each sample is computed then the sampling distribution of sample proportion follows a Normal distribution.
The mean of this sampling distribution of sample proportion is:
The standard deviation of this sampling distribution of sample proportion is:
The information provided is:
<em>N</em> = 675
<em>X</em>₁ = bodies with low vitamin-D levels had weak bones
<em>n</em>₁ = 82
<em>p</em>₁ = 0.085
<em>X</em>₂ = bodies with regular vitamin-D levels had weak bones
<em>n</em>₂ = 593
<em>p</em>₂ = 0.01
Both the sample sizes are large enough, i.e. <em>n</em>₁ = 82 > 30 and <em>n</em>₂ = 593 > 30.
So, the central limit theorem can be applied to approximate the sampling distribution of sample proportions by the Normal distribution.
Thus, a normal model is a good fit for the sampling distribution.
The vertex form: y = a(x - h)² + k
y = ax² + bx + c then h = -b/2a and k = f(h).
y = x² - 12x + 8
a = 1; b = -12; c = 8
h = -(-12)/(2·1) = 12/2 = 6
k = f(6) = 6² - 12·6 + 8 = 36 - 72 + 8 = -28
Answer: y = (x - 6)² - 28.
Y=10°
If we're solving for the ninety degree angle, which I seem to doubt, then if 3x=90 then 2x=60 giving a missing total of 30. 30/3 equals 10