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11Alexandr11 [23.1K]
3 years ago
6

Find the nth term of 1,4,3,16,5,36,7

Mathematics
1 answer:
MakcuM [25]3 years ago
3 0

Answer:

64,9

Step-by-step explanation:

From my understanding the pattern would be:

Every other number is the next consecutive odd number,  

( 1, 3, 5, 7 ) are all of the consecutive odds. (So the next odd is 9.  )

That when we get to the part of where we get every other number for the remaining numbers, if you multiply the next even number by itself (square it).  

4, 16, and 36 ( they are all perfect squares.  )

They are the perfect squares of ( 2, 4, and 6) . So it makes sense that ( 8 ) is the next even number and you would square that and get ( 64 )

I hope this makes sense!!

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Step-by-step explanation:

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3 years ago
How do you do the second part of the problem?
Sveta_85 [38]

Answer:

8

Step-by-step explanation:

According to the Alternating Series Estimation Theorem:

│aₙ₊₁│≤ ε

1 / (4 (n + 1)⁴) ≤ 0.00005

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7 0
3 years ago
Kenya bought 4 boxes of
Andreas93 [3]

Answer:

she will be recivining $24.06

Step-by-step explanation:

4x3.99=15.96

2x4.99=9.98

15.96+9.98=25.94

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Of course with out taxes

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3 years ago
If a cotton candy vendor has 16 blue cones which is 50% of the total number of cones, then how many TOTAL cones does he have i n
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Answer:

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Step-by-step explanation:

5 0
2 years ago
According to data released by FiveThirty Eight (data drawn on Monday, August 17th, 2020), Donald Trump wins an Electoral College
sineoko [7]

Answer:

a) P = 0.274925

b) required confidence interval = (0.2705589, 0.2793344)

c) FALSE

d) FALSE

e) TRUE

f) There is still probability that he would win. And it would be highly unusual if he wins assuming that the true population proportion is 0.274925.

Step-by-step explanation:

a)

PROBABILITY

since total number of simulations is 40,000 and and number of times Donald Trump wins an Electoral College majority in the 2020 US Presidential Election is  10,997

so the required Probability will be 10,997 divided by 40,000

P = 10997 / 40000 = 0.274925

b)

To get 95% confidence interval for the parameter in question a

(using R)

>prop.test(10997,40000)

OUTPUT

1 - Sample proportion test with continuity correction

data: 10997 out of 40000, null probability 0.5

x-squared = 8104.5, df = 1, p-value < 2.23-16

alternative hypothesis : true p ≠ 0.5

0.2705589  0.2793344

sample estimate

p

0.274925

∴ required confidence interval = (0.2705589, 0.2793344)

c)

FALSE

This is a wrong interpretation of a confidence interval. It indicates that there is 95% chance that the confidence interval you calculated contains the true proportion. This is because when you perform several times, 95% of those intervals would contain the true proportion but as the confidence intervals will vary so you can't say that the true proportion is in any interval with 95% probability.

d)

FALSE

Once again, this is a wrong interpretation of a confidence interval. The confidence interval tells us about the population parameter and not the sample statistic.

e)

TRUE

This is a correct interpretation of a confidence interval. It indicates that if we perform sampling with same sample size (40000) several times and calculate the 95% confidence interval of population proportion for each of them, then 95% of these confidence interval should contain the population parameter.

f)

The simulation results obtained doesn't always comply with the true population. Also, result of one simulation can't be taken for granted. We need several simulations to come to a conclusion. So, we can never ever guarantee based on a simulation result to say that Donald Trump 'Won't' or 'Shouldn't' win.

There is still probability that he would win. And it would be highly unusual if he wins assuming that the true population proportion is 0.274925.

5 0
3 years ago
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