N / 2
400 / 2 = 200
200 / 2 = 100
100 / 2 = 50
50 / 2 = 25
25 / 2 = 12.5
Answer:
P-l/2 = w
Step-by-step explanation:
First you divide both sides by 2 then you subtract L from both sides leaving you with P minus L / 2 equals W.
This one bc there’s one point vertically and there’s no more than one point when you draw a vertical line down
Answer:
D
Step-by-step explanation:
Solution:-
The standard sinusoidal waveform defined over the domain [ 0 , 2π ] is given as:
f ( x ) = sin ( w*x ± k ) ± b
Where,
w: The frequency of the cycle
k: The phase difference
b: The vertical shift of center line from origin
We are given that the function completes 2 cycles over the domain of [ 0 , 2π ]. The number of cycles of a sinusoidal wave is given by the frequency parameter ( w ).
We will plug in w = 2. No information is given regarding the phase difference ( k ) and the position of waveform from the origin. So we can set these parameters to zero. k = b = 0.
The resulting sinusoidal waveform can be expressed as:
f ( x ) = sin ( 2x ) ... Answer
Answer:
a) ![\mu_{\bar x} =\mu = 276.1](https://tex.z-dn.net/?f=%20%5Cmu_%7B%5Cbar%20x%7D%20%3D%5Cmu%20%3D%20276.1)
![\sigma_{\bar x} =\frac{\sigma}{\sqrt{n}}=\frac{34.4}{\sqrt{64}}=4.3](https://tex.z-dn.net/?f=%5Csigma_%7B%5Cbar%20x%7D%20%3D%5Cfrac%7B%5Csigma%7D%7B%5Csqrt%7Bn%7D%7D%3D%5Cfrac%7B34.4%7D%7B%5Csqrt%7B64%7D%7D%3D4.3)
b) From the central limit theorem we know that the distribution for the sample mean
is given by:
c)
Step-by-step explanation:
Let X the random variable the represent the scores for the test analyzed. We know that:
![\mu=E(X) = 276.1 , \sigma=Sd(X) = 34.4](https://tex.z-dn.net/?f=%20%5Cmu%3DE%28X%29%20%3D%20276.1%20%2C%20%5Csigma%3DSd%28X%29%20%3D%2034.4)
And we select a sample size of 64.
The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".
Part a
For this case the mean and standard error for the sample mean would be given by:
![\mu_{\bar x} =\mu = 276.1](https://tex.z-dn.net/?f=%20%5Cmu_%7B%5Cbar%20x%7D%20%3D%5Cmu%20%3D%20276.1)
![\sigma_{\bar x} =\frac{\sigma}{\sqrt{n}}=\frac{34.4}{\sqrt{64}}=4.3](https://tex.z-dn.net/?f=%5Csigma_%7B%5Cbar%20x%7D%20%3D%5Cfrac%7B%5Csigma%7D%7B%5Csqrt%7Bn%7D%7D%3D%5Cfrac%7B34.4%7D%7B%5Csqrt%7B64%7D%7D%3D4.3)
Part b
From the central limit theorem we know that the distribution for the sample mean
is given by:
Part c
For this case we want this probability:
![P(\bar X \geq 285)](https://tex.z-dn.net/?f=%20P%28%5Cbar%20X%20%5Cgeq%20285%29)
And we can use the z score defined as:
![z=\frac{\bar x -\mu}{\sigma_{\bar x}}](https://tex.z-dn.net/?f=%20z%3D%5Cfrac%7B%5Cbar%20x%20-%5Cmu%7D%7B%5Csigma_%7B%5Cbar%20x%7D%7D)
And using this we got:
And using a calculator, excel or the normal standard table we have that: