Answer:
check the screenshot I attached:
Answer:
The right answer is:
a.H0: μd = 0; H1: μd > 0
Step-by-step explanation:
The claim that want to be tested is that the sales were significantly increased after the commercial, indicanting that the advertisement campaign was effective.
This claim is usually expressed in the alternative hypothesis as it has to have enough evidence to prove that it is true.
Then, the alternative hypothesis H1 should state that the difference (sales after - sales before) is higher than 0.
The null hypothesis would state that the difference is not significantly different from 0, or, in other words, that the sales are the same before and after and that the variation is due to pure chance.
Then, the null hypothesis H0 would state that the difference is equal to 0.
The right answer is:
a.H0: μd = 0; H1: μd > 0
I conclude that the sum will be even because any even number can be represented by 2n where n is a whole number
and even numbers are 2 apart, so
the sum of the first 15 are
2n+2(n+1)+2(n+2) etc until we get to 2(n+14)
we can undistribute the 2 from all of them and get
2(n+n+1+n+2...n+14)
and we are sure that whatever is in the parenthasees is a whole number because whole+whole=whole
therefor, the sum is even
if you did want to find the sum then
an=2n
the 15th even number is 30
the first is 2
S15=15(2+30)/2=15(32)/2=15(16)=240
which is even
Answer:
(a) Shown below
(b) There is a positive relation between the number of assemblers and production.
(c) The correlation coefficient is 0.9272.
Step-by-step explanation:
Let <em>X</em> = number of assemblers and <em>Y</em> = number of units produced in an hour.
(a)
Consider the scatter plot below.
(b)
Based on the scatter plot it can be concluded that there is a positive relationship between the variables <em>X</em> and <em>Y</em>, i.e. as the value of <em>X</em> increases <em>Y</em> also increases.
(c)
The formula to compute the correlation coefficient is:
![r=\frac{n\sum XY-\sum X\sum Y}{\sqrt{[n\sum X^{2}-(\sum X)^{2}][n\sum Y^{2}-(\sum Y)^{2}]}} }](https://tex.z-dn.net/?f=r%3D%5Cfrac%7Bn%5Csum%20XY-%5Csum%20X%5Csum%20Y%7D%7B%5Csqrt%7B%5Bn%5Csum%20X%5E%7B2%7D-%28%5Csum%20X%29%5E%7B2%7D%5D%5Bn%5Csum%20Y%5E%7B2%7D-%28%5Csum%20Y%29%5E%7B2%7D%5D%7D%7D%20%7D)
Compute the correlation coefficient between <em>X</em> and <em>Y</em> as follows:
![r=\frac{n\sum XY-\sum X\sum Y}{\sqrt{[n\sum X^{2}-(\sum X)^{2}][n\sum Y^{2}-(\sum Y)^{2}]}} }=\frac{(5\times430)-(15\times120)}{\sqrt{[(5\times55)-15^{2}][(5\times3450)-120^{2}]}} =0.9272](https://tex.z-dn.net/?f=r%3D%5Cfrac%7Bn%5Csum%20XY-%5Csum%20X%5Csum%20Y%7D%7B%5Csqrt%7B%5Bn%5Csum%20X%5E%7B2%7D-%28%5Csum%20X%29%5E%7B2%7D%5D%5Bn%5Csum%20Y%5E%7B2%7D-%28%5Csum%20Y%29%5E%7B2%7D%5D%7D%7D%20%7D%3D%5Cfrac%7B%285%5Ctimes430%29-%2815%5Ctimes120%29%7D%7B%5Csqrt%7B%5B%285%5Ctimes55%29-15%5E%7B2%7D%5D%5B%285%5Ctimes3450%29-120%5E%7B2%7D%5D%7D%7D%20%3D0.9272)
Thus, the correlation coefficient is 0.9272.
Answer:
Correct choice is A
Step-by-step explanation:
The range of the function are all possible values which variable y can take.
From the given diagram you can see that the variable x changes from -4 to the 9. So the domain of the function is 
Also the variable y takes its values from -4 to 8. Thus, the range is 