Answer:
The second table of values.
Step-by-step explanation:
Let's put the x-values in the second table of values in correct number order:
x: -3, -2, -1, 0, 1
Now, let's write out the y-values in correct number order:
y: 1/4, 1, 4, 16, 64
Finally, let's rewrite the second table of values with the x-values in order and the corresponding y-values underneathe:
x: -3, -2, -1 0 1
y: 64, 16, 4, 1, 1/4
As it can be seen, as the x-values get bigger in value, the y-values get smaller exponentially, which is the definition of exponential decay.
160000000 =
move the decimal so only one number is to the left
we need to move it 8 times
1.60000000 *10^8
1.6*10^8
58413000000
move the decimal so only one number is to the left
5.8413000000 * 10^10
5.8413*10^10
<u>Explanation:</u>
a) First, note that the Type I error refers to a situation where the null hypothesis is rejected when it is actually true. Hence, her null hypothesis would be H0: mean daily demand of her clothes in this region should be greater than or equal to 100.
The implication of Type I error in this case is that Mary <u>rejects</u> that the mean daily demand of her clothes in this region is greater than or equal to 100 when it is actually true.
b) While, the Type II error, in this case, is a situation where Mary accepts the null hypothesis when it is actually false. That is, Mary <u>accepts</u> that the mean daily demand of her clothes in this region is greater than or equal to 100 when it is actually false.
c) The Type I error would be important to Mary because it shows that she'll be having a greater demand (which = more sales) for her products despite erroneously thinking otherwise.
y = a ( x - h)^2 + v
a is positive when the parabola opens up and negative when it opens down.
