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sergeinik [125]
2 years ago
6

You’ve bought a half-dozen (six) eggs from the store but you forgot to check them first! The probability that no eggs are broken

is 0.4. Otherwise, any number of broken eggs is equally probable. Define the random variable X = the number of broken eggs.
(a) Write out the pmf of X.
(b) Compute the probability that an even number of eggs is broken.

(c) Compute the expectation and variance of X.
Mathematics
1 answer:
GREYUIT [131]2 years ago
7 0

Answer:

a)

P(X = 0) = C_{6,0}.(0.1416)^{0}.(0.8584)^{6} = 0.4

P(X = 1) = C_{6,1}.(0.1416)^{1}.(8584)^{5} = 0.3960

P(X = 2) = C_{6,2}.(0.1416)^{2}.(8584)^{4} = 0.1633

P(X = 3) = C_{6,3}.(0.1416)^{3}.(8584)^{3} = 0.0359

P(X = 4) = C_{6,4}.(0.1416)^{4}.(8584)^{2} = 0.0044

P(X = 5) = C_{6,5}.(0.1416)^{5}.(8584)^{1} = 0.0003

P(X = 6) = C_{6,6}.(0.1416)^{6}.(8584)^{0} = 0.00001

b) 56.77% probability that an even number of eggs is broken.

c)

Expectation: 0.8496

Variance: 0.7293

Step-by-step explanation:

For each egg, there are only two possible outcomes. Either it is broken, or it is not. The probability of an egg being broken is independent from other eggs. So we use the binomial probability distribution to solve this question.

Binomial probability distribution

The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.

P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}

In which C_{n,x} is the number of different combinations of x objects from a set of n elements, given by the following formula.

C_{n,x} = \frac{n!}{x!(n-x)!}

And p is the probability of X happening.

The expected value of the binomial distribution is:

E(X) = np

The variance of the binomial distribution is:

V(X) = np(1-p)

There are 6 eggs

So n = 6

The probability that no eggs are broken is 0.4.

This means that P(X = 0) = 0.4. So

P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}

P(X = 0) = C_{6,0}.p^{0}.(1-p)^{6}

(1 - p)^{6} = 0.4

Taking the sixth root from both sides of the equality

(1 - p) = 0.8584

p = 0.1416

Each egg has a 0.1416 probability of being broken

(a) Write out the pmf of X.

Probability of each value, from 0 to 6

P(X = 0) = C_{6,0}.(0.1416)^{0}.(0.8584)^{6} = 0.4

P(X = 1) = C_{6,1}.(0.1416)^{1}.(8584)^{5} = 0.3960

P(X = 2) = C_{6,2}.(0.1416)^{2}.(8584)^{4} = 0.1633

P(X = 3) = C_{6,3}.(0.1416)^{3}.(8584)^{3} = 0.0359

P(X = 4) = C_{6,4}.(0.1416)^{4}.(8584)^{2} = 0.0044

P(X = 5) = C_{6,5}.(0.1416)^{5}.(8584)^{1} = 0.0003

P(X = 6) = C_{6,6}.(0.1416)^{6}.(8584)^{0} = 0.00001

(b) Compute the probability that an even number of eggs is broken.

0, 2, 4 or 6

P = P(X = 0) + P(X = 2) + P(X = 4) + P(X = 6) = 0.4 + 0.1633 + 0.0044 + 0.00001 = 0.5677

56.77% probability that an even number of eggs is broken.

(c) Compute the expectation and variance of X.

Expectation:

E(X) = np = 6*0.1416 = 0.8496

Variance:

V(X) = np(1-p) = 6*0.1416*0.8584 = 0.7293

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A researcher gathers data on the length of essays​ (number of​ lines) and the SAT scores received for a sample of students enrol
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(C) The slope of the regression equation is not significantly different from zero

Step-by-step explanation:

Let's suppose that we have the following linear model:

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In order to estimate the coefficients \beta_0 ,\beta_1 we can use least squares estimation.

If we are interested in analyze if we have a significant relationship between the dependent and the independent variable we can use the following system of hypothesis:

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Alternative hypothesis: \beta_1 \neq 0

Or in other wouds we want to check is our slope is significant.

In order to conduct this test we are assuming the following conditions:

a) We have linear relationship between Y and X

b) We have the same probability distribution for the variable Y with the same deviation for each value of the independent variable

c) We assume that the Y values are independent and the distribution of Y is normal

The significance level is provided and on this case is \alpha=0.05

The standard error for the slope is given by this formula:

SE_{\beta_1}=\frac{\sqrt{\frac{\sum (y_i -\hat y_i)^2}{n-2}}}{\sqrt{\sum (X_i -\bar X)^2}}

Th degrees of freedom for a linear regression is given by df=n-2 since we need to estimate the value for the slope and the intercept.

In order to test the hypothesis the statistic is given by:

t=\frac{\hat \beta_1}{SE_{\beta_1}}

The confidence interval for the slope would be given by this formula:

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And using the last formula we got that the confidence interval for the slope coefficient is given by:

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Answer:

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Step-by-step explanation:

The distance between two points can be found by using the formula

d =  \sqrt{( {x1 - x2})^{2} +  ({y1 - y2})^{2}  }  \\

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