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Alik [6]
3 years ago
8

What is the explicit rule for the geometric sequence? 600, 300, 150, 75, ...

Mathematics
1 answer:
pochemuha3 years ago
5 0

Answer:

Step-by-step explanation:

Hello, this is a geometric sequence so we are looking for a multiplicative factor.

a_0=600\\\\a_1=a_0 \cdot \boxed{\dfrac{1}{2}} = 300 = 600 \cdot \boxed{\dfrac{1}{2}}\\\\a_2=a_1 \cdot \boxed{\dfrac{1}{2}} = 150= 300 \cdot \boxed{\dfrac{1}{2}}\\\\a_3=a_2 \cdot \boxed{\dfrac{1}{2}} = 75 = 150 \cdot \boxed{\dfrac{1}{2}}

So, the explicit formula is for n

\boxed{a_n=a_0\cdot \left(\dfrac{1}{2}\right)^n=600\cdot \left(\dfrac{1}{2}\right)^n}}

Hope this helps.

Do not hesitate if you need further explanation.

Thank you

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Determine the mean absolute<br> deviation 8, 0, 4, 7, 4, 5, 3, 3
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5 0
3 years ago
Brewed decaffeinated coffee contains some caffeine. We want to estimate the amount of caffeine in 8 ounce cups of decaf coffee a
rusak2 [61]

Answer:

We would have to take a sample of 62 to achieve this result.

Step-by-step explanation:

Confidence level of 95%.

We have that to find our \alpha level, that is the subtraction of 1 by the confidence interval divided by 2. So:

\alpha = \frac{1 - 0.95}{2} = 0.025

Now, we have to find z in the Ztable as such z has a pvalue of 1 - \alpha.

That is z with a pvalue of 1 - 0.025 = 0.975, so Z = 1.96.

Now, find the margin of error M as such

M = z\frac{\sigma}{\sqrt{n}}

In which \sigma is the standard deviation of the population and n is the size of the sample.

Assume that the standard deviation in the amount of caffeine in 8 ounces of decaf coffee is known to be 2 mg.

This means that \sigma = 2

If we wanted to estimate the true mean amount of caffeine in 8 ounce cups of decaf coffee to within /- 0.5 mg, how large a sample would we have to take to achieve this result?

We would need a sample of n.

n is found when M = 0.5. So

M = z\frac{\sigma}{\sqrt{n}}

0.5 = 1.96\frac{2}{\sqrt{n}}

0.5\sqrt{n} = 2*1.96

Dividing both sides by 0.5

\sqrt{n} = 4*1.96

(\sqrt{n})^2 = (4*1.96)^2

n = 61.5

Rounding up

We would have to take a sample of 62 to achieve this result.

8 0
3 years ago
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