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Degger [83]
3 years ago
12

PLEASE HELP NEED ANSWER ASAP In a probability experiment, Karen flipped a coin 106 times. The coin landed on heads 48 times. Wha

t percentage of the coin flips resulted in tails? Round to the nearest percent.
Mathematics
2 answers:
drek231 [11]3 years ago
4 0
I believe the answer is 48%. :)
Tatiana [17]3 years ago
4 0
Your question is related to the binomial distribution. The question doesn't mention if this is a fair coin. So I assume the probability of getting ahead in a single toss = P. As a result, the probability of getting a tail in a single toss = (1-P).The probability of getting 22 heads and 28 tails = C(22,28)*(P^22)*[(1-P)^28]Where C(x,y) is the combination calculator/operator. The idea can be substantially generalized. If we toss a coin n times, and the probability of a head on any toss is P  (which need not be equal to 1/2, the coin could be unfair), then the probability of exactly k heads  
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Evgesh-ka [11]

Answer:

a.) The sum of the weights of the two in insects is 0.0031 grams. (0.0031 grams)

b.) The fly is 0.0013 grams heavier than the gnat. (0.0013 grams)

Step-by-step explanation:

2.2 * 10^-3 = 2.2 * 1/1000 which is 2.2/1000.

9 * 10^-4 = 9 * 1/10000 = 9/10000

To add 9/10000 to 2.2/1000 we have to find the common denominator, which will be 10000.

So we do:

2.2/1000 * 10/10 = 22/10000

9/10000 + 22/10000 = 31/10000 = 0.0031.

The sum of the weights of the two in insects is 0.0031 grams.

To find how much heavier the fly is than the gnat we do:

22/10000 - 9/10000 = 13/10000 = 0.0013

The fly is 0.0013 grams heavier than the gnat.

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Natalka [10]
Since the others are congruent and linear so is a and b!!!!!!!!
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trapecia [35]
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Answer:

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Step-by-step explanation:

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Step-by-step explanation:

First, note that a flexible statistical learning method refers to using models that take into account agree difference in the observed data set, and are thus adjustable. While the inflexible method usually involves a model that has no regard to the kind of data set.

a) The sample size n is extremely large, and the number of predictors p is small. (BETTER)

In this case since the sample size is extremely large a flexible model is a best fit.

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In such case overfiting the data is more likely because of of the small observations.

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The flexible method would be a better fit.

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In such case, using a flexible model is a best fit for the error terms because it can be adjusted.

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