Answer:
a. Fail to reject the null hypothesis.
Step-by-step explanation:
Here we test the claim that proportion of reachers is really 41%
![H_0: p =0.41\\H_a: p \neq 0.41](https://tex.z-dn.net/?f=H_0%3A%20p%20%3D0.41%5C%5CH_a%3A%20p%20%5Cneq%200.41)
(Two tailed test at 1% significance level)
Observed proportion = 0.36
Sample size n = 100
Std error of proportion = ![\sqrt{\frac{pq}{n} } \\=\sqrt{\frac{0.41*0.59}{100} } \\=0.0492](https://tex.z-dn.net/?f=%5Csqrt%7B%5Cfrac%7Bpq%7D%7Bn%7D%20%7D%20%5C%5C%3D%5Csqrt%7B%5Cfrac%7B0.41%2A0.59%7D%7B100%7D%20%7D%20%5C%5C%3D0.0492)
p difference = 0.36-0.41 =- 0.05
Test statistic Z = p diff/std error =-1.0166
p value=0.286
Since p >0.01 we accept null hypothesis.
a. Fail to reject the null hypothesis.
Answer:
The numbers of doors that will have no blemishes will be about 6065 doors
Step-by-step explanation:
Let the number of counts by the worker of each blemishes on the door be (X)
The distribution of blemishes followed the Poisson distribution with parameter
/ door
The probability mass function on of a poisson distribution Is:
![P(X=x) = \dfrac{e^{- \lambda } \lambda ^x}{x!}](https://tex.z-dn.net/?f=P%28X%3Dx%29%20%3D%20%5Cdfrac%7Be%5E%7B-%20%5Clambda%20%7D%20%5Clambda%20%5Ex%7D%7Bx%21%7D)
![P(X=x) = \dfrac{e^{- \ 0.5 }( 0.5)^ x}{x!}](https://tex.z-dn.net/?f=P%28X%3Dx%29%20%3D%20%5Cdfrac%7Be%5E%7B-%20%5C%200.5%20%7D%28%200.5%29%5E%20x%7D%7Bx%21%7D)
The probability that no blemishes occur is :
![P(X=0) = \dfrac{e^{- \ 0.5 }( 0.5)^ 0}{0!}](https://tex.z-dn.net/?f=P%28X%3D0%29%20%3D%20%5Cdfrac%7Be%5E%7B-%20%5C%200.5%20%7D%28%200.5%29%5E%200%7D%7B0%21%7D)
![P(X=0) = 0.60653](https://tex.z-dn.net/?f=P%28X%3D0%29%20%3D%200.60653)
P(X=0) = 0.6065
Assume the number of paints on the door by q = 10000
Hence; the number of doors that have no blemishes is = qp
=10,000(0.6065)
= 6065
Answer:
9/13 = 0.6923
Step-by-step explanation:
We start by defining
A as event that head was flipped
B1 = event that coin is biased
B2 = event that it is unbiased
P(B1) = 3/5
P(B2) = 2/5
P(A|B1) = 3/4
P(A|B2) = 2/4 = 1/2
When we solve this using bayes theorem we have to find
p(B1|A) = [P(B1) x P(A|B1)]/[P(B1) x P(A|B1) + P(B2) x P(A|B2)
= 0.6 x 0.75 / 0.6 x 0.75 + 0.4x0.5
= 0.45/0.45+0.2
= 0.45/0.65
= 0.6923
Answer:
1,3
Step-by-step explanation: The slope of this graph is 1,3
well it is going to be 10,000,000 (000-0000 to 999-9999) minus any number before 100-0000 that would make 9,000,000 legal numbers.
i would have to guess