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1 year ago

6

Ten cards are numbered 1 through 10, and one card is chosen at random. What is the probability that one card has a number greate

r than or equal to 4?

1 answer:

Mashutka [201]1 year ago

3 0

Answer:

7/10

Step-by-step explanation:

4,5,6,7,8,9,10

7 numbers counting four so 7/10

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kodGreya [7K]

Answer:

9. 16.34

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2 years ago

Charles has $1600 dollars in his account and makes automatic $400

kipiarov [429]

1600(-400)= f is the y axis and -400 because its taking money from his account

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2 years ago

USA Today reports that about 25% of all prison parolees become repeat offenders. Alice is a social worker whose job is to counse

pychu [463]

Answer:

a) $P(X=0)=(4C0)(0.75)^0 (1-0.75)^{4-0}=0.0039$

$P(X=1)=(4C1)(0.75)^1 (1-0.75)^{4-1}=0.0469$

$P(X=2)=(4C2)(0.75)^2 (1-0.75)^{4-2}=0.211$

$P(X=3)=(4C3)(0.75)^2 (1-0.75)^{4-3}=0.422$

$P(X=4)=(4C4)(0.75)^2 (1-0.75)^{4-4}=0.316$

b) $E(X) = np = 4*0.75=3$

c) $Sd(X) =\sqrt{np(1-p)}=\sqrt{4*0.75*(1-0.75)}=0.866$

d) $P(X \geq 3) \geq 0.98$

And the dsitribution that satisfy this is $X\sim Binom(n=9,p=0.75$

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".

Let X the random variable of interest, on this case we now that:

$X \sim Binom(n=4, p=1-0.25=0.75)$

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

$P(X=0)=(4C0)(0.75)^0 (1-0.75)^{4-0}=0.0039$

$P(X=1)=(4C1)(0.75)^1 (1-0.75)^{4-1}=0.0469$

$P(X=2)=(4C2)(0.75)^2 (1-0.75)^{4-2}=0.211$

$P(X=3)=(4C3)(0.75)^2 (1-0.75)^{4-3}=0.422$

$P(X=4)=(4C4)(0.75)^2 (1-0.75)^{4-4}=0.316$

Part b

The expected value is givn by:

$E(X) = np = 4*0.75=3$

Part c

For the standard deviation we have this:

$Sd(X) =\sqrt{np(1-p)}=\sqrt{4*0.75*(1-0.75)}=0.866$

Part d

For this case the sample size needs to be higher or equal to 9. Since we need a value such that:

$P(X \geq 3) \geq 0.98$

And the dsitribution that satisfy this is $X\sim Binom(n=9,p=0.75$

We can verify this using the following code:

"=1-BINOM.DIST(3,9,0.75,TRUE)" and we got 0.99 and the condition is satisfied.

4 0

2 years ago

A machine has 12 identical components which function independently. The probability that a component will fail is 0.2. The machi

iogann1982 [59]

Answer:

$P(X>3) =1- P(X\leq 3) = 1-[P(X=0)+P(X=1) +P(X=2) +P(X=3)]$

And if we find the individual probabilities we got:

$P(X=0)=(12C0)(0.2)^0 (1-0.2)^{12-0}=0.0687$

$P(X=1)=(12C1)(0.2)^1 (1-0.2)^{12-1}=0.2062$

$P(X=2)=(12C2)(0.2)^2 (1-0.2)^{12-2}=0.2835$

$P(X=3)=(12C3)(0.2)^3 (1-0.2)^{12-3}=0.2362$

And if we replace we got:

$P(X>3) =1- P(X\leq 3) = 1-[0.0687+0.2062 +0.2835 +0.2362]=0.20543$

Step-by-step explanation:

Previous concepts

A Bernoulli trial is "a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted". And this experiment is a particular case of the binomial experiment.

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".

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:

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

The complement rule is a theorem that provides a connection between the probability of an event and the probability of the complement of the event. Lat A the event of interest and A' the complement. The rule is defined by: $P(A)+P(A') =1$

Solution to the problem

Let X the random variable of interest, on this case we now that:

$X \sim Binom(n=12, p=0.2)$

And we want to find this probability:

$P(X > 3)$

And we can use the complement rule and we got:

$P(X>3) =1- P(X\leq 3) = 1-[P(X=0)+P(X=1) +P(X=2) +P(X=3)]$

And if we find the individual probabilities we got:

$P(X=0)=(12C0)(0.2)^0 (1-0.2)^{12-0}=0.0687$

$P(X=1)=(12C1)(0.2)^1 (1-0.2)^{12-1}=0.2062$

$P(X=2)=(12C2)(0.2)^2 (1-0.2)^{12-2}=0.2835$

$P(X=3)=(12C3)(0.2)^3 (1-0.2)^{12-3}=0.2362$

And if we replace we got:

$P(X>3) =1- P(X\leq 3) = 1-[0.0687+0.2062 +0.2835 +0.2362]=0.20543$

4 0

2 years ago

Read 2 more answers

Find the area of the shaded region in the figure below.<br> 7 m

tester [92]

Answer:

the rigth arel

Step-by-step explanation:

4 0

2 years ago