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tigry1 [53]
2 years ago
6

According to New Jersey Transit, the 8:00 A.M. weekday train from Princeton to New York City has a 90% chance of arriving on tim

e on a randomly selected day. Suppose this claim is true. Choose 6 days at random. Let W
Mathematics
1 answer:
jonny [76]2 years ago
4 0

Answer:

See explanation below.

Step-by-step explanation:

Assuming this problem: "According to New Jersey Transit, the 8:00 A.M. weekday train from Princeton to New York City has a 90% chance of arriving on time on a randomly selected day. Suppose this claim is true. Choose 6 days at random. Let W = the number of days on which the train arrives late. "

For this case we can check if the binomial model can be used checking conditions:

1) We satisfy that we have independent trials and is assumed for this case

2) We have a fixed value for the trials n =6 and for the probability p = 0.9 on each trial

3) We have bernoulli experiments on each trial since we have success or failure for each case.

So then since all the conditions are satisfied we can assume that the binomial model can be used here.

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 W the random variable of interest "the number of days on which the train arrives late", on this case we now that:

W \sim Binom(n=6, p=0.9)

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!}

We can find all the possible probabilities for all the possible values of X like this:

P(X=0)=(6C0)(0.9)^0 (1-0.9)^{6-0}=1x10^{-6}

P(X=1)=(6C1)(0.9)^1 (1-0.9)^{6-1}=5.4x10^{-5}

P(X=2)=(6C2)(0.9)^2 (1-0.9)^{6-2}=0.001215

P(X=3)=(6C3)(0.9)^3 (1-0.9)^{6-3}=0.01458

P(X=4)=(6C4)(0.9)^4 (1-0.9)^{6-4}=0.0984

P(X=5)=(6C5)(0.9)^5 (1-0.9)^{6-5}=0.354

P(X=5)=(6C5)(0.9)^6 (1-0.9)^{6-6}=0.531

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$250 c+ $ 180 g > $ 950

<u>Step-by-step explanation:</u>

As a cryptographer (c), Miyoko earns per day = $ 250

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The equation can be rewritten to find c as, (950-180 g) / 250

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The population p of a small community on the outskirts of a city grows rapidly over a 20-year period: t05101520p1002004509502000
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Answer:

The population of the small community, 5 years into the future, after the initial 20-year period = 4268.

Step-by-step explanation:

t | 0 | 5 | 10 | 15 | 20

p | 100 | 200 | 450 | 950 | 2000

The exponential function will look like

p = aeᵏᵗ

where a and k are constants.

Take the natural logarithms of both sides

In p = In aeᵏᵗ

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In p | 4.605 | 5.298 | 6.109 | 6.856 | 7.601

In p = kt + In a.

y = mx + b

m = k and b = In a

Performing a linear regression analysis on the now-linear relationship between In p and t and also plotting a graph of the variables.

The regression equation obtained is

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The first attached image shows the equations necessary for the estimation of the linear regression parameters.

The second attached image shows the use of regression calculator and the plot of the function In p versus t.

Comparing

y = 0.151x + 4.584

With

In p = kt + In a.

y = In p

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a = 97.905

The exponential function relating p and t,

p = aeᵏᵗ now becomes

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So, to predict the population 5 years into the future, that is 5 years after the 20 year period.

we need p at t=25 years.

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Hope this Helps!!!

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Answer:

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Step-by-step explanation:

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