Answer:
32cm
Step-by-step explanation:
Let the width be w. Now we know that the width is 16cm less than the length, this means that the length will thus have a total length of w + 16
Now, we know that the perimeter of a rectangle is 2(l + b)
This means 2(w + w + 16) = 160
4w + 32 = 160
4w = 128
w = 128/4 = 32cm
Part a)
It was given that 3% of patients gained weight as a side effect.
This means
![p = 0.03](https://tex.z-dn.net/?f=p%20%3D%200.03)
![q = 1 - 0.03 = 0.97](https://tex.z-dn.net/?f=q%20%3D%201%20-%200.03%20%3D%200.97)
The mean is
![\mu = np](https://tex.z-dn.net/?f=%20%5Cmu%20%20%3D%20np)
![\mu = 643 \times 0.03 = 19.29](https://tex.z-dn.net/?f=%20%5Cmu%20%3D%20643%20%5Ctimes%200.03%20%3D%2019.29)
The standard deviation is
![\sigma = \sqrt{npq}](https://tex.z-dn.net/?f=%20%5Csigma%20%3D%20%20%5Csqrt%7Bnpq%7D%20)
![\sigma = \sqrt{643 \times 0.03 \times 0.97}](https://tex.z-dn.net/?f=%20%5Csigma%20%3D%20%20%5Csqrt%7B643%20%5Ctimes%200.03%20%5Ctimes%200.97%7D%20)
![\sigma =4.33](https://tex.z-dn.net/?f=%20%5Csigma%20%3D4.33)
We want to find the probability that exactly 24 patients will gain weight as side effect.
P(X=24)
We apply the Continuity Correction Factor(CCF)
P(24-0.5<X<24+0.5)=P(23.5<X<24.5)
We convert to z-scores.
![P(23.5 \: < \: X \: < \: 24.5) = P( \frac{23.5 - 19.29}{4.33} \: < \: z \: < \: \frac{24.5 - 19.29}{4.33} ) \\ = P( 0.97\: < \: z \: < \: 1.20) \\ = 0.051](https://tex.z-dn.net/?f=P%2823.5%20%5C%3A%20%3C%20%5C%3A%20X%20%5C%3A%20%3C%20%5C%3A%2024.5%29%20%3D%20P%28%20%5Cfrac%7B23.5%20-%2019.29%7D%7B4.33%7D%20%5C%3A%20%3C%20%5C%3A%20z%20%5C%3A%20%3C%20%5C%3A%20%20%5Cfrac%7B24.5%20-%2019.29%7D%7B4.33%7D%20%29%20%5C%5C%20%20%3D%20P%28%200.97%5C%3A%20%3C%20%5C%3A%20z%20%5C%3A%20%3C%20%5C%3A%20%201.20%29%20%5C%5C%20%20%3D%200.051)
Part b) We want to find the probability that 24 or fewer patients will gain weight as a side effect.
P(X≤24)
We apply the continuity correction factor to get;
P(X<24+0.5)=P(X<24.5)
We convert to z-scores to get:
![P(X \: < \: 24.5) = P(z \: < \: \frac{24.5 - 19.29}{4.33} ) \\ = P(z \: < \: 1.20) \\ = 0.8849](https://tex.z-dn.net/?f=P%28X%20%5C%3A%20%3C%20%5C%3A%2024.5%29%20%3D%20P%28z%20%5C%3A%20%3C%20%5C%3A%20%20%5Cfrac%7B24.5%20-%2019.29%7D%7B4.33%7D%20%29%20%20%5C%5C%20%3D%20%20%20P%28z%20%5C%3A%20%3C%20%5C%3A%201.20%29%20%20%5C%5C%20%20%3D%200.8849)
Part c)
We want to find the probability that
11 or more patients will gain weight as a side effect.
P(X≥11)
Apply correction factor to get:
P(X>11-0.5)=P(X>10.5)
We convert to z-scores:
![P(X \: > \: 10.5) = P(z \: > \: \frac{10.5 - 19.29}{4.33} ) \\ = P(z \: > \: - 2.03)](https://tex.z-dn.net/?f=P%28X%20%5C%3A%20%3E%20%5C%3A%2010.5%29%20%3D%20P%28z%20%5C%3A%20%3E%20%5C%3A%20%20%5Cfrac%7B10.5%20-%2019.29%7D%7B4.33%7D%20%29%20%20%5C%5C%20%3D%20P%28z%20%5C%3A%20%3E%20%5C%3A%20%20-%202.03%29)
![= 0.9788](https://tex.z-dn.net/?f=%20%3D%200.9788)
Part d)
We want to find the probability that:
between 24 and 28, inclusive, will gain weight as a side effect.
P(24≤X≤28)=
P(23.5≤X≤28.5)
Convert to z-scores:
![P(23.5 \: < \: X \: < \: 28.5) = P( \frac{23.5 - 19.29}{4.33} \: < \: z \: < \: \frac{28.5 - 19.29}{4.33} ) \\ = P( 0.97\: < \: z \: < \: 2.13) \\ = 0.1494](https://tex.z-dn.net/?f=P%2823.5%20%20%5C%3A%20%20%3C%20%20%5C%3A%20X%20%5C%3A%20%20%3C%20%20%5C%3A%2028.5%29%20%3D%20P%28%20%5Cfrac%7B23.5%20-%2019.29%7D%7B4.33%7D%20%20%20%5C%3A%20%20%3C%20%20%5C%3A%20z%20%5C%3A%20%20%3C%20%20%5C%3A%20%20%5Cfrac%7B28.5%20-%2019.29%7D%7B4.33%7D%20%29%20%5C%5C%20%20%3D%20P%28%200.97%5C%3A%20%20%3C%20%20%5C%3A%20z%20%5C%3A%20%20%3C%20%20%5C%3A%202.13%29%20%5C%5C%20%20%3D%200.1494)
75000. one hundread thousand more would be 100,000 more that what is given making it 175,000
Answer:
Step-by-step explanation:
![\text{The yellow area is half of the area of the square minus the area of the circle.}\\ \\ A_s=6^2=36\\ \\ A_c=\pi r^2=\pi 3^2=9\pi \\ \\ A_y=\frac{1}{2}(36-9\pi)\approx 3.9in^2](https://tex.z-dn.net/?f=%5Ctext%7BThe%20yellow%20area%20is%20half%20of%20the%20area%20of%20the%20square%20minus%20the%20area%20of%20the%20circle.%7D%5C%5C%20%5C%5C%20A_s%3D6%5E2%3D36%5C%5C%20%5C%5C%20A_c%3D%5Cpi%20r%5E2%3D%5Cpi%203%5E2%3D9%5Cpi%20%5C%5C%20%5C%5C%20A_y%3D%5Cfrac%7B1%7D%7B2%7D%2836-9%5Cpi%29%5Capprox%203.9in%5E2)
Answer and explanation:
Null hypothesis(H0) says sentiment for increasing speed limit in the two populations is the same
Alternative hypothesis(Ha) says sentiment for increasing speed limit in the two populations is different
Where p1 is the first population proportion =65/297= 0.22
And p2 is the second population proportion = 78/189= 0.41
P= p1+p2/n1+n2= 65+78/297+189= 0.29
Hence H0= p1-p2=0
Ha=p1-p2≠0
Test statistic= p1-p2/√p(1-p) (1/n1+1/n2)
= 0.22-0.41/√0.29(1-0.29)(1/297+1/189)
= -4.49
Critical value at 95% significance level= 1.96( from tables)
We therefore reject null hypothesis as critical value is greater than test statistic
Therefore the sentiment for increasing speed limit in the two populations is different
b. at proportions of 0.24 and 0.40 for p1 and p2 respectively
Test statistic = 0.24-0.40/0.29(1-0.29)(1/297+1/189)
= -3.78
P value is 0.0002 at 0.05 significance level
Hence probability =0.4998