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leonid [27]
3 years ago
6

A machine that cuts corks for wine bottles operates in such a way that the distribution of the diameter of the corks produced is

well approximated by a normal distribution with mean 4 cm and standard deviation 0.1 cm. The specifications call for corks with diameters between 3.85 and 4.15 cm. A cork not meeting the specifications is considered defective. (A cork that is too small leaks and causes the wine to deteriorate; a cork that is too large doesn't fit in the bottle.) What proportion of corks produced by this machine are defective? (Round the answer to four decimal places.)

Mathematics
1 answer:
Norma-Jean [14]3 years ago
4 0

Answer:

A. P(x<3.85 or x>4.15)= P(x<3.85)+P(x>4.15) = 0.1336

Step-by-step explanation:

Working with an ordinary Normal Distribution of probability and trying to find the probabilities asked in it could be difficult, because there´s no easy method to find probabilities in a generic Normal Distribution (with mean μ=4 and STD σ=0.1). The recommended approach to this question is to use a process called "Normalize", this process let us translate the problem of any Normal Distribution to a Standard Normal Distribution (μ=0 and σ=1) where there´s easier ways to find probabilities in there. The "Normalization" goes as follows:

Suppose you want to know P(x<a) of the Normal Distribution you are working with:

P(x<a)=P( (x-μ)/σ < (a-μ)/σ )=P(z<b)   ( b=(a-μ)/σ )

Where μ is the mean and σ is the STD of your Normal Distribution. Notice P(z<b) now it´s a probability in a Standard Normal Distribution, now we can find it using the available method to do so. My favorite is a chart (It´s attached to this answer) that contains a lot of probabilities in a Standard Normal Distribution. Let´s solve this as an example

A. We want to find the probability of the cork being defective (P(x<3.85) + P(x>4.15)). Now we find those separated and, then, add them for our answer.

Let´s begin with P(x<3.85), we start by normalizing that probability:

P(x<3.85)= P( (x-μ)/σ < (3.85-4)/0.1 )= P(z<-1.5)

And now it´s time to use the chart, it works like this: If you want P(z<c) and the decimal expansion of c=a.bd... , then:

P(z<c)=(a.b , d)

Where (a.b , d) are the coordinates of the probability in the chart. Keep in mind that will only work with "<" (It won´t work directly with P(z>c)) and we will do some extra work in those cases.

P(z<-1.5) is in the coordinates (-1.5 , 0)

P(z<-1.5)= 0.0668

P(x<3.85)= 0.0668

Now we are looking for P(x>4.15), let´s Normalize it too:

P(x>4.15)=P( (x-μ)/σ < (4.15-4)/0.1 )=P(z>1.5)

But remember the chart only work with "<", so we need to use a property of probability:

P(z>1.5)= 1 - P(z<1.5)

Using the chart:

P(z<1.5)=0.9332                             (1.5 , 0)

P(z>1.5)= 1 - 0.9332

P(z>1.5)= 0.0668

P(x>4.15)= 0.0668

And our final answer will be:

P(x<3.85 or x>4.15)= P(x<3.85)+P(x>4.15) = 0.1336

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Leto [7]

Answer:

The Depth of water in the bathtub initially = 2 cm

Step-by-step explanation:

For this question, we want to f8nd the amount of water present in the bathtub at time t = 0 using line of best fit, that is, linear regression analysis.

The table provided shows that the depth of water in the bathtub (y) changes as time (x) progresses.

Time (min) (x) | 2 | 4 | 6 | 8 | 10 | 12 | | 14

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Running the analysis on a spreadsheet application, like excel, the table of parameters is obtained and presented in the first attached image to this solution.

Σxᵢ = sum of all the independent variables (sum of all the time data)

Σyᵢ = sum of all the dependent variables (sum of all the depth data)

Σxᵢyᵢ = sum of the product of each dependent variable and its corresponding independent variable

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The scatter plot and the line of best fit is presented in the second attached image to this solution

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Slope; m = [n×Σxᵢyᵢ - (Σxᵢ)×(Σyᵢ)] / [nΣxᵢ² - (∑xi)²]

Intercept b = [Σyᵢ - m×(Σxᵢ)] / n

Mean of x = (Σxᵢ)/n

Mean of y = (Σyᵢ) / n

Sample correlation coefficient r: r =

[n*Σxᵢyᵢ - (Σxᵢ)(Σyᵢ)] ÷ {√([n*Σxᵢ² - (Σxᵢ)²][n*Σyᵢ² - (Σyᵢ)²])}

And -1 ≤ r ≤ +1

All of these formulas are properly presented in the third attached image to this answer

The table of results; mean of x, mean of y, intercept, slope, regression equation and sample coefficient is presented in the fourth attached image to this answer.

The linear regression equation obtained is then

y = 3.911x + 1.857

at a regression coefficient of 0.987 (the closer the value is to 1, the more accurate the equation obtained)

So, the Depth of water in the bathtub initially, that is, when t = x= 0,

y = 3.911x + 1.857

y = 3.911(0) + 1.857 = 1.857 cm = 2 cm to the nearest integer.

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3) Check

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