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ki77a [65]
3 years ago
12

1) How many seconds are in three days?

Mathematics
2 answers:
Natasha_Volkova [10]3 years ago
7 0

Answer:

259200 seconds

The conversion factor from days to seconds is 86400, which means that 1 day is equal to 86400 seconds:

1 d = 86400 s

To convert 3 days into seconds we have to multiply 3 by the conversion factor in order to get the time amount from days to seconds. We can also form a simple proportion to calculate the result:

1 d → 86400 s

3 d → T(s)

Solve the above proportion to obtain the time T in seconds:

T(s) = 3 d × 86400 s

T(s) = 259200 s

The final result is:

3 d → 259200 s

We conclude that 3 days is equivalent to 259200 seconds:

3 days = 259200 seconds

<em><u>Hope this helps :)</u></em>

morpeh [17]3 years ago
3 0

Answer:

Answer

Step-by-step explanation:

259200

days-->hours-->min-->seconds

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In a particular faculty 60% of students are men and 40% are women. In a random sample of 50 students what is the probability tha
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Answer:

a) The expected value is given by:

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And we got:

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c) 1) Random sample (assumed)

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Since the 3 conditions are satisfied we can use the normal approximation:

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P(X>25)= P(X>25.5) = 1-P(X \leq 25.5)= 1-P(Z

Step-by-step explanation:

Previous concepts

The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".

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X \sim Binom(n=50, p=0.4)

The probability mass function for the Binomial distribution is given as:

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

Where (nCx) means combinatory and it's given by this formula:

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

Part a

The expected value is given by:

E(X) = np = 50*0.4 = 20

and the variance is given by:

Var(X) =np(1-p) = 50*0.4*(1-0.4) = 12

Part b

For this case we want to find this probability:

P(X>25)= 1-P(X\leq 25)

And we can find this probability with the following Excel code:

=1-BINOM.DIST(25,50,0.4,TRUE)

And we got:

P(X>25)= 1-P(X\leq 25)=0.0573

Part c

1) Random sample (assumed)

2) np= 50*0.4= 20 >10

n(1-p) =50*0.6= 30>10

3) Independence (assumed)

Since the 3 conditions are satisfied we can use the normal approximation:

X \sim N(\mu = 20 , \sigma= 3.464)

Part d

We want this probability:

P(X>25) = 1-P(Z< \frac{25-20}{3.464}) = 1-P(z

Part e

For this case we use the continuity correction and we have this:

P(X>25)= P(X>25.5) = 1-P(X \leq 25.5)

P(X>25)= P(X>25.5) = 1-P(X \leq 25.5)= 1-P(Z

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