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DENIUS [597]
3 years ago
13

For one toss of a certain coin, the probability that the outcome is heads is 0.6. If this coin is tossed 5 times, which of the f

ollowing is the probability that the outcome will be heads at least 4 times?
Mathematics
1 answer:
pishuonlain [190]3 years ago
5 0

Answer:

The probability is 0.33696

Step-by-step explanation:

The probability that the outcome will be heads x times is calculated using the following equation:

P(x) = nCx*p^{x} *(1-p)^{n-x}

nCx is calculated as:

nCx=\frac{n!}{x!(n-x)!}

This apply for variables that follows a binomial distribution. In which we have n independent and identical events with two possibles results: success and fail with a probability p and 1-p respectively.

So, In this case, n is equal to 5, and p is equal to 0.6 because we are going to call success the event in which the outcome of the coin is head.

Then, the probability that the outcome will be heads at least 4 times is calculated as:

P = P(4) + P(5)

Where P(4) is:

P(4) = 5C4*0.6^{4} *(1-0.6)^{5-4}

P(4)=0.2592

And P(5) is:

P(5) = 5C5*0.6^{5} *(1-0.6)^{5-5}

P(4)=0.07776

Finally, the probability is:

P = 0.2592 + 0.07776

P = 0.33696

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Vladimir [108]

Answer:

#18)  <em>m</em>∠B = 107°

#19) <em>m</em>∠B = 17°

Step-by-step explanation:

*Supplementary angles are angles who's measures equal 180°

*Complementary angles are angles who's measures equal 90°

#18) If ∠A is 73°, then we must subtract 73 from 180.

180 - 73 = 107

This means that <em>m</em>∠B = 107°.

#19) If ∠A is 73°, then we must subtract 73 from 90.

90 - 73 = 17

This means that <em>m</em>∠B = 17°.

8 0
3 years ago
Suppose that the data for analysis includes the attributeage. Theagevalues for the datatuples are (in increasing order) 13, 15,
Bas_tet [7]

Answer:

a) \bar X = \frac{\sum_{i=1}^{27} X_i }{27}= \frac{809}{27}=29.96

Median = 25

b) Mode = 25, 35

Since 25 and 35 are repeated 4 times, so then the distribution would be bimodal.

c) Midrange = \frac{70+13}{3}=41.5

d) Q_1 = \frac{20+21}{2} =20.5

Q_3 =\frac{35+35}{2}=35

e) Min = 13 , Q1 = 20.5, Median=25, Q3= 35, Max = 70

f) Figura attached.

g) When we use a quantile plot is because we want to show the percentage or the fraction of values below or equal to an specified value for the distribution of the data.

By the other hand the quantile-quantile plot shows the quantiles of the distribution values against other selected distribution (specified, for example the normal distribution). If the points are on a straight line we assume that the data values fit very well to the hypothetical distribution selected.

Step-by-step explanation:

For this case w ehave the following dataset given:

13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30,33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70.

Part a

The mean is calculated with the following formula:

\bar X = \frac{\sum_{i=1}^{27} X_i }{27}= \frac{809}{27}=29.96

The median on this case since we have 27 observations and that represent an even number would be the 14 position in the dataset ordered and we got:

Median = 25

Part b

The mode is the most repeated value on the dataset on this case would be:

Mode = 25, 35

Since 25 and 35 are repeated 4 times, so then the distribution would be bimodal.

Part c

The midrange is defined as:

Midrange = \frac{Max+Min}{2}

And if we replace we got:

Midrange = \frac{70+13}{3}=41.5

Part d

For the first quartile we need to work with the first 14 observations

13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25

And the Q1 would be the average between the position 7 and 8 from these values, and we got:

Q_1 = \frac{20+21}{2} =20.5

And for the third quartile Q3 we need to use the last 14 observations:

25, 30,33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70

And the Q3 would be the average between the position 7 and 8 from these values, and we got:

Q_3 =\frac{35+35}{2}=35

Part e

The five number summary for this case are:

Min = 13 , Q1 = 20.5, Median=25, Q3= 35, Max = 70

Part f

For this case we can use the following R code:

> x<-c(13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30,33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70)

> boxplot(x,main="boxplot for the Data")

And the result is on the figure attached. We see that the dsitribution seems to be assymetric. Right skewed with the Median<Mean

Part g

When we use a quantile plot is because we want to show the percentage or the fraction of values below or equal to an specified value for the distribution of the data.

By the other hand the quantile-quantile plot shows the quantiles of the distribution values against other selected distribution (specified, for example the normal distribution). If the points are on a straight line we assume that the data values fit very well to the hypothetical distribution selected.

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Rama09 [41]

Answer:

the answer is 7

Step-by-step explanation:

8d +7=63

-7. -7

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8d = 56

__. ___

8. 8

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Answer:

6

Step-by-step explanation:

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4 times -2 = -8

4 times -4 = -16

12(-8/-16)

-8/-16 = 0.5

so 12(0.5) = 6

4 0
3 years ago
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