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igor_vitrenko [27]
3 years ago
6

A boardwalk is to be erected along an ocean beach. The specifications for the boardwalk require a width of at least 155 cm., but

the pre-cut pieces of lumber supplied for the boardwalk are uniformly distributed in length between 148 cm. and 164 cm.
Mathematics
1 answer:
sleet_krkn [62]3 years ago
7 0

Answer:

a) X \sim Unif (a=148, b=164)

b) E(X) = \frac{a+b}{2}= \frac{148+164}{2}=156 cm

c) Var(X)= \frac{(b-a)^2}{12}= \frac{(164-148)^2}{12}= \frac{64}{3}= 21.33

Sd(X)= \sqrt{21.33}= 4.6188

d) P(X

e) P(155< X< 160)= F(160)-F(155)= \frac{160-148}{16} -\frac{155-148}{16}= 0.75-0.4375=0.3125Step-by-step explanation:

Assuming the following questions:

a. The distribution is X Use whole numbers.

Let X the random variable that represent the "lenght of pieces" used for the construction. We know that X follows an uniform distribution given by:

X \sim Unif (a=148, b=164)

b. The average length of the pieces of lumber is cm. Use whole numbers.

For this case the expected value for the distribution is given by:

E(X) = \frac{a+b}{2}= \frac{148+164}{2}=156 cm

c. Find the standard deviation.

First we need to calculate the variance given by:

Var(X)= \frac{(b-a)^2}{12}= \frac{(164-148)^2}{12}= \frac{64}{3}= 21.33

Sd(X)= \sqrt{21.33}= 4.6188

d. What's the probability that a randomly chosen piece of lumbar will be less than the required lenght?

For this case we can use the density function for X given by:

f(x) =\frac{1}{b-a}= \frac{1}{164-148}= \frac{1}{16} , 148 \leq X \leq 164

And the cumulative distribution function would be given by:

F(x) =\frac{x-148}{16} , 148 \leq X\leq 164

We want to find this probability:

P(X

e. Find the probability that a randomly chosen piece of lumber will be between 155 and 160 cm long?

For this case we want this probability:

P(155< X< 160)

And we can use the cdf function and we have:

P(155

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Given:

Cards labelled 1, 3, 5, 6, 8 and 9.

A card is drawn and not replaced. Then a second card is drawn at random.

To find:

The probability of drawing 2 even numbers.

Solution:

We have,

Even number cards = 6, 8

Odd numbers cards = 1, 3, 5, 9

Total cards =  1, 3, 5, 6, 8 and 9

Number of even cards = 2

Number of total cards = 6

So, the probability of getting an even card in first draw is:

P_1=\dfrac{\text{Number of even cards}}{\text{Number of total cards}}

P_1=\dfrac{2}{6}

P_1=\dfrac{1}{3}

Now,

Number of remaining even cards = 1

Number of remaining cards = 5

So, the probability of getting an even card in second draw is:

P_2=\dfrac{\text{Number of remaining even cards}}{\text{Number of remaining total cards}}

P_2=\dfrac{1}{5}

The probability of drawing 2 even numbers is:

P=P_1\times P_2

P=\dfrac{1}{3}\times \dfrac{1}{5}

P=\dfrac{1}{15}

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2 years ago
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A real estate agent has 19 properties that she shows. She feels that there is a 30% chance of selling any one property during a
netineya [11]

Answer:

P(X \geq 5)=1-P(X

We can find the individual probabilities:

P(X=0)=(19C0)(0.3)^0 (1-0.3)^{19-0}=0.00114

P(X=1)=(19C1)(0.3)^1 (1-0.3)^{19-1}=0.0092

P(X=2)=(19C2)(0.3)^2 (1-0.3)^{19-2}=0.0358

P(X=3)=(19C3)(0.3)^3 (1-0.3)^{19-3}=0.0869

P(X=4)=(19C4)(0.3)^4 (1-0.3)^{19-4}=0.1491

And replacing we got:

P(X \geq 5) = 1-[0.00114+0.009282+0.0358+0.0869+0.149]= 0.7178

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".

Solution to the problem

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

X \sim Binom(n=19, p=0.3)

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!}

And we want to find this probability:

P(X \geq 5)

And we can use the complement rule:

P(X \geq 5)=1-P(X

We can find the individual probabilities:

P(X=0)=(19C0)(0.3)^0 (1-0.3)^{19-0}=0.00114

P(X=1)=(19C1)(0.3)^1 (1-0.3)^{19-1}=0.0092

P(X=2)=(19C2)(0.3)^2 (1-0.3)^{19-2}=0.0358

P(X=3)=(19C3)(0.3)^3 (1-0.3)^{19-3}=0.0869

P(X=4)=(19C4)(0.3)^4 (1-0.3)^{19-4}=0.1491

And replacing we got:

P(X \geq 5) = 1-[0.00114+0.009282+0.0358+0.0869+0.149]= 0.7178

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