Respuesta :
Answer:
1.) very large sample size
2.) 0.11326
3.) np and n(1-p) should be ≥ 10
4.) 0.010809
Step-by-step explanation:
Given :
p = 0.65 ; n = 500
2.)
P(x ≤ 300)
P(x =x) = nCx * p^x * (1 - p)^(n - x)
Using a binomial probability calculator :
P(x ≤ 300) = 0.11326
To justify why if it can be approximated using a normal distributon :
np and n(1-p) should be ≥ 10
np = 500 * 0.65 = 325
n(1-p) = 500(1 - 0.65) = 175
4.) probability using normal distribution estimate:
Mean, m = np = 500 * 0.65 = 325
Standard deviation, s = sqrt(n*p*(1-p)) = sqrt(500*0.65*(1 - 0.65)) = sqrt(113.75) = 10.665
x ≤ 300
Correction:
x = x + 0.5 = 300 - 0.5 = 300.5
P(x ≤ 300.5)
Zscore = (x - m) / s
Zscore = (300.5 - 325) / 10.665
Zscore = - 24.5 / 10.665
Zscore = - 2.297
P(Z ≤ - 2.297) = 0.010809
X can be modeled by a binomial distribution even though the sample was selected without replacement because the sample size is too large, and the probability that 300 or fewer high school students in the sample eat breakfast before going to school is 0.010809.
Given :
- Suppose that 65% of high school students eat breakfast before going to school.
- A random sample of 500 high school students was surveyed.
- Let X = the number of high school students in a random sample of size 500 who eat breakfast before going to school.
1) X can be modeled by a binomial distribution even though the sample was selected without replacement because the sample size is too large.
2) The formula for the binomial distribution is given by:
[tex]\rm P (x=x)= \; ^nC_x \times p^x \times (1-p)^{n-x}[/tex]
Now, substitute the known terms in the above formula.
[tex]\rm P (x\leq 300)= \; ^{500}C_x \times (0.65)^x \times (1-0.65)^{500-x}[/tex]
Simplify the above expression.
[tex]\rm P(x \leq 300)=0.11326[/tex]
3) Now, the value of np and n(1 - p) is given below:
np = 500 [tex]\times[/tex] 0.65 = 325
n(1 - p) = 500 [tex]\times[/tex] (1 - 0.65) = 175
X can be approximated by a Normal distribution because the value of np and n(1 - p) is greater than or equal to 10.
4)
First, determine the standard deviation.
[tex]\rm \sigma = \sqrt{np(1-p)}[/tex]
[tex]\rm \sigma = \sqrt{500\times 0.65 \times (1-0.65)}[/tex]
[tex]\rm \sigma = 10.665[/tex]
Now, the z-score can be calculated by using the formula:
[tex]\rm z =\dfrac{X-\mu}{\sigma}[/tex]
[tex]\rm z =\dfrac{300.5-325}{10.665}[/tex]
z = -2.297
So, the probability that 300 or fewer high school students in the sample eat breakfast before going to school is given by:
[tex]\rm P(Z\leq -2.297) = 0.010809[/tex]
For more information, refer to the link given below:
https://brainly.com/question/795909