The gas mieages (in miles per gallon) for 32 cars are shown in the frequency distribution. Approximate the mean of the frequency distribution. (in The approximate mean of the frequency distribution is

Ahmed rolls a die twice and added the face values. Compute the following: i) The probability that the sum is greater than 8 is ii) The probability that the sum is an even number is.

In order to solve the problem we are given, let’s find out all the possible outcomes of Ahmed rolling a die twice. In rolling a die once, the probability of getting any number from 1 to 6 is 1/6 each. Therefore, in rolling a die twice, we have 6 × 6 = 36 possible outcomes.Explanation:We need to find the probability that the sum is greater than 8. Now, let’s see what pairs of numbers will give us a sum greater than 8.

These are: (3, 6), (4, 5), (5, 4), and (6, 3). So there are 4 such pairs and each pair can occur in two ways, giving a total of 8 ways for the sum to be greater than 8.  the probability of getting a sum greater than 8 is: 8/36 = 2/9Now, we need to find the probability that the sum is an even number. Let’s see what pairs of numbers will give us an even sum. These are: (1, 1), (1, 3), (1, 5), (2, 2), (2, 4), (2, 6), (3, 1), (3, 3), (3, 5), (4, 2), (4, 4), (4, 6), (5, 1), (5, 3), (5, 5), (6, 2), (6, 4), and (6, 6).

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i) The probability that the sum is greater than 8 is 5/18.

ii) The probability that the sum is an even number is 1/2.

To compute the probabilities requested, we need to consider all the possible outcomes of rolling a die twice and summing the face values.

There are a total of 36 equally likely outcomes when rolling a die twice (6 possibilities for the first roll and 6 possibilities for the second roll). Let's analyze each case:

i) The probability that the sum is greater than 8:

The possible outcomes with a sum greater than 8 are:

{(3, 6), (4, 5), (4, 6), (5, 4), (5, 5), (5, 6), (6, 3), (6, 4), (6, 5), (6, 6)}

There are 10 favorable outcomes. Therefore, the probability is given by:

P(sum > 8) = 10/36 = 5/18

ii) The probability that the sum is an even number:

The possible outcomes with an even sum are:

{(1, 1), (1, 3), (1, 5), (2, 2), (2, 4), (2, 6), (3, 1), (3, 3), (3, 5), (4, 2), (4, 4), (4, 6), (5, 1), (5, 3), (5, 5), (6, 2), (6, 4), (6, 6)}

There are 18 favorable outcomes. Therefore, the probability is given by:

P(sum is even) = 18/36 = 1/2

To summarize:

i) The probability that the sum is greater than 8 is 5/18.

ii) The probability that the sum is an even number is 1/2.

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We will substitute this value of sin²x in our expression which will give;6 tan²x(1 - sin²x)6 tan²x(1 - (1 - cos²x))6 tan²x cos²x.

We need to simplify the given expression which is given below;

6 tan2 x − 6 tan2 x sin2 x

In order to solve this expression, we will first write it in a factored form which will be;

6 tan²x(1 - sin²x)

We know that the identity for sin²x is;sin²x + cos²x = 1

Which can be rearranged to give;

sin²x = 1 - cos²x

Now we will substitute this value of sin²x in our expression which will give;6 tan²x(1 - sin²x)6 tan²x(1 - (1 - cos²x))6 tan²x cos²x.

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A table of percentiles that takes sample size into consideration is not required.Therefore, option C is not right when the sample kurtosis coefficient in Excel is negative.

If the sample kurtosis coefficient in Excel is negative, we can make certain inferences. These are the inferences we can make if the sample kurtosis coefficient in Excel is negative:We know that the population is platykurtic. When the sample kurtosis coefficient is negative, the distribution is flat-topped, which means that there are fewer outliers in the distribution. As a result, the population is platykurtic.

We can deduce that the population is flat and that there are fewer extreme values (tails) than a normal distribution.We know that the population is leptokurtic. When a sample kurtosis coefficient is negative, the tails of the population distribution are shorter than the tails of a normal distribution, indicating that the population is leptokurtic. It has more values than a standard normal distribution that fall in the extreme ranges.

We should consult a table of percentiles that takes sample size into consideration. There is no need to seek a table of percentiles that takes sample size into consideration. Because the sample kurtosis coefficient is negative, we can infer that the population is either platykurtic or leptokurtic.  Thus, option C is the incorrect option.

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To find the covariance between X and Y, we need to use the joint moment generating function (MGF) and the properties of MGFs.

The joint MGF MX,Y(t1, t2) is given as:

[tex]MX,Y(t1, t2) = \frac{1}{3}(1 + e^{t1 + 2t2} + e^{2t1 + t2})[/tex]

To find the covariance, we need to differentiate the joint MGF twice with respect to t1 and t2, and then evaluate it at t1 = 0 and t2 = 0.

First, let's differentiate MX,Y(t1, t2) with respect to t1:

[tex]\frac{\partial^2(MX,Y(t1, t2))}{\partial t1^2} = \frac{\partial}{\partial t1}\left(\frac{\partial(MX,Y(t1, t2))}{\partial t1}\right)\\\\= \frac{\partial}{\partial t_1} \left(\frac{\partial}{\partial t_1} \left(\frac{1}{3} (1 + e^{t_1 + 2t_2} + e^{2t_1 + t_2})\right)\right)\\\\= \frac{\partial}{\partial t1}\left(\frac{1}{3}(2e^{t1 + 2t2} + 2e^{2t1 + t2})\right)\\\\= \frac{2}{3}(2e^{t1 + 2t2} + 4e^{2t1 + t2})[/tex]

Now, let's differentiate MX,Y(t1, t2) with respect to t2:

[tex]\frac{\partial^2(MX,Y(t1, t2))}{\partial t2^2} = \frac{\partial}{\partial t2}\left(\frac{\partial(MX,Y(t1, t2))}{\partial t2}\right)\\\\= \frac{\partial}{\partial t_2} \left(\frac{\partial}{\partial t_2} \left(\frac{1}{3} (1 + e^{t_1 + 2t_2} + e^{2t_1 + t_2})\right)\right)\\\\= \frac{\partial}{\partial t2}\left(\frac{1}{3}(4e^{t1 + 2t2} + 2e^{2t1 + t2})\right)\\\\= \frac{2}{3}(4e^{t1 + 2t2} + 2e^{2t1 + t2})[/tex]

Now, we can evaluate the second derivatives at t1 = 0 and t2 = 0:

[tex]\frac{\partial^2(MX,Y(t1, t2))}{\partial t1^2} = \frac{2}{3}(2e^{0 + 2(0)} + 4e^{2(0) + 0})\\\\= \frac{2}{3}(2 + 4)\\\\= 2\\\\\\\frac{\partial^2(MX,Y(t1, t2))}{\partial t2^2} = \frac{2}{3}(4e^{0 + 2(0)} + 2e^{2(0) + 0})\\\\= \frac{2}{3}(4 + 2)\\\\= \frac{4}{3}[/tex]

Finally, the covariance between X and Y is given by:

[tex]Cov(X, Y) = \frac{\partial^2(MX,Y(t1, t2))}{\partial t1^2} - \frac{\partial^2(MX,Y(t1, t2))}{\partial t2^2}\\\\= 2 - \frac{4}{3}\\\\= \frac{6}{3} - \frac{4}{3}\\\\= \frac{2}{3}[/tex]

Therefore, the covariance between X and Y is [tex]\frac{2}{3}[/tex].

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The probabilities are,

(a)  P(X ≤ 50) = 1

(b) P(X ≤ 40) = 0.75

(c) P(40 ≤ X ≤ 60) = 0.25

(d) P(X < 0) = 0

(e) P(0 ≤ X < 10) = 0.25

(f) P(-10 < X < 10) = 0.25

a) For P(X ≤ 50):

We have to add the probabilities of all the values of X that are less than or equal to 50.

Since F(x) = 1 when x is greater than or equal to 50, we have,

⇒ P(X ≤ 50) = P(X < -10) + P(-10 ≤ X < 30) + P(30 ≤ X < 50) + P(X ≥ 50)

⇒ P(X ≤ 50) = 0 + 0.25 + 0.75 + 1

⇒ P(X ≤ 50) = 2

Since, probabilities cannot be greater than 1.

Therefore, the correct answer is,

⇒ P(X ≤ 50) = P(X < -10) + P(-10 ≤ X < 30) + P(30 ≤ X < 50) + P(X ≤ 50)

⇒ P(X ≤ 50) = 0 + 0.25 + 0.75 + 0

⇒ P(X ≤ 50) = 1

So, the probability that X is less than or equal to 50 is 1.

b) For P(X ≤ 40):

We have to add the probabilities of all the values of X that are less than or equal to 40.

Since F(x) = 0.75 when x is greater than or equal to 30 and less than 50, and F(x) = 1 when x is greater than or equal to 50, we have,

⇒ P(X ≤ 40) = P(X < -10) + P(-10 ≤ X < 30) + P(30 ≤ X ≤ 40)

⇒ P(X ≤ 40) = 0 + 0.25 + 0.5

⇒ P(X ≤ 40) = 0.75

So, the probability that X is less than or equal to 40 is 0.75.

c) For P(40 ≤ X ≤ 60):

To find P(40 ≤ X ≤ 60), we have to subtract the probability of X being less than 40 from the probability of X being less than or equal to 60.

Since F(x) = 1 when x is greater than or equal to 50, we have,

⇒ P(40 ≤ X ≤ 60) = P(X ≤ 60) - P(X ≤ 40)

⇒ P(40 ≤ X ≤ 60) = 1 - 0.75

⇒ P(40 ≤ X ≤ 60) = 0.25

So, the probability that X is between 40 and 60 (inclusive) is 0.25.

d) For P(X < 0):

To find P(X < 0), we have to add the probabilities of all the values of X that are less than 0. Since F(x) = 0 when x is less than -10, we have,

⇒ P(X < 0) = P(X < -10)

⇒ P(X < 0) = 0

So, the probability that X is less than 0 is 0.

e) For P(0 ≤ X < 10):

To find P(0 ≤ X < 10), we have to subtract the probability of X being less than 0 from the probability of X being less than or equal to 10.

Since F(x) = 0.25 when x is greater than or equal to -10 and less than 30, we have,

⇒ P(0 ≤ X < 10) = P(X ≤ 10) - P(X < 0)

⇒ P(0 ≤ X < 10) = P(X ≤ 10)

⇒ P(0 ≤ X < 10) = F(10)

⇒ P(0 ≤ X < 10) = 0.25

So, the probability that X is between 0 (inclusive) and 10 (exclusive) is 0.25.

f) For P(-10 < X < 10):

To find P(-10 < X < 10), we have to subtract the probability of X being less than or equal to -10 from the probability of X being less than or equal to 10.

Since F(x) = 0.25 when x is greater than or equal to -10 and less than 30, we have,

⇒ P(-10 < X < 10) = P(X ≤ 10) - P(X ≤ -10)

⇒ P(-10 < X < 10) = F(10) - F(-10)

⇒ P(-10 < X < 10) = 0.25 - 0

⇒ P(-10 < X < 10) = 0.25

So, the probability that X is between -10 (exclusive) and 10 (exclusive) is 0.25.

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The complete question is attached below:

The correlation coefficient is 0.3161 (rounded to two decimal places).

Correlation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables.

To find the correlation coefficient using the given information xx=38,

yy=32

and xy=11, we need to use the formula for correlation coefficient:

[tex]r=\frac{S_{xy}}{\sqrt{S_{xx}}\sqrt{S_{yy}}}[/tex]

Where r is the correlation coefficient,

Sxy is the sum of the cross-products,

Sxx is the sum of squares of x deviations, and

Syy is the sum of squares of y deviations.

Substituting the given values in the above formula, we have

[tex]r=\frac{S_{xy}}{\sqrt{S_{xx}}\sqrt{S_{yy}}}[/tex]

[tex]r=\frac{11}{\sqrt{38}\sqrt{32}}$$$$[/tex]

[tex]r=\frac{11}{\sqrt{1216}}$$$$[/tex]

=[tex]0.3161$$[/tex]

Thus, the correlation coefficient is 0.3161 (rounded to two decimal places).

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By analyzing the given sample, we find that the mean age of the stock-brokers is approximately 52.6, the median age is 51, and there is no mode since no age appears more than once.

To review the distribution of the ages of the stock-brokers, we can analyze the given sample of ages: 53, 42, 63, 70, 35, 47, 55, 58, 41, 49, 44, 61.

One way to analyze the distribution is by calculating measures of central tendency, such as the mean, median, and mode.

Mean:

To find the mean, we sum up all the ages and divide by the total number of brokers (25 in this case):

Mean = (53 + 42 + 63 + 70 + 35 + 47 + 55 + 58 + 41 + 49 + 44 + 61) / 25 = 52.6

Median:

The median is the middle value when the ages are arranged in ascending order. In this case, the ages in ascending order are: 35, 41, 42, 44, 47, 49, 53, 55, 58, 61, 63, 70.

Since there are 12 values, the median is the average of the 6th and 7th values:

Median = (49 + 53) / 2 = 51

Mode:

The mode is the value that appears most frequently in the data. In this case, there is no value that appears more than once, so there is no mode.

These measures help provide an understanding of the central tendency and distribution of the ages in the sample.

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Note: The complete question is - A large investment firm wants to review the distribution of the ages of its stock-brokers. The ages of a sample of 25 brokers are as follows: 53 42 63 70 35 47 55 58 41 51 44 61 20 57 46 49 58 29 48 42 36 39 52 45 56. a/ Construct a relative frequency histogram for the data, using five class intervals and the value 20 as the lower limit of the 1st class, the value 70 as the upper limit of the 5th class. b/ What proportion of the total area under the histogram fall between 30 and 50, inclusive?

We are given a sample of size 67, the sample mean as 43.1 and the standard deviation as 13.6. The 98% confidence interval is [39.28, 46.92].

We need to find the 98% confidence interval.

The formula for the confidence interval for a population mean when the population standard deviation is known is as follows:

Confidence interval = sample mean ± z* (σ/√n)

where σ is the population standard deviation, n is the sample size, z* is the z-score associated with the desired level of confidence.

For 98% confidence interval, the z-value is 2.33 (from the z-table)

Substituting the given values, we get:

Confidence interval = 43.1 ± 2.33 * (13.6/√67)≈ 43.1 ± 3.82

Therefore, the correct answer is [39.28, 46.92].

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To find the probability that x is greater than or equal to 62.48, we can use the standard normal distribution. First, we need to standardize the value of 62.48 using the z-score formula:

z = (x - μ) / σ

Where x is the value, μ is the mean, and σ is the standard deviation.

In this case, we have:

x = 62.48

μ = 20

σ = 18

z = (62.48 - 20) / 18

z = 2.36

Now, we can find the probability using a standard normal distribution table or a calculator. The probability of x being greater than or equal to 62.48 is the same as the probability of z being greater than or equal to 2.36.

Using a standard normal distribution table or calculator, we find that the probability is approximately 0.0091 or 0.91%.

Therefore, p(x ≥ 62.48) is approximately 0.0091 or 0.91%.

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Let g be a continuous, positive, decreasing function on [1,oo). We need to compare the values of the integral of the following options provided below:2.BCA3.ABC4.

ASince g is a decreasing function on [1, oo), we can show that ∫[n,n+1] g(x)dx ≥ g(n+1) for every positive integer n.Using this inequality and adding them all up gives us∫1n g(x)dx≥∑n=1∞ g(n)Therefore, the series ∑n=1∞ g(n) diverges (the terms are positive and do not go to zero), so the integral of option BCA is infinite.Option ABC is equal to∫1∞ g(x)dx=∫11g(x)dx+∫12g(x)dx+∫23g(x)dx+⋯+∫n,n+1g(x)dx+⋯

Since g is a positive function, we have 0 ≤∫n,n+1g(x)dx≤g(n)so the integral is bounded below by ∑n=1∞ g(n) which diverges. Thus the integral of option ABC is also infinite.Option A is equal to∫2∞g(x)dx=∫23g(x)dx+⋯+∫n,n+1g(x)dx+⋯and since g is a decreasing function, we have ∫n,n+1g(x)dx≤g(n+1)(n+1−n)=g(n+1)so the integral is bounded above by∑n=1∞g(n+1)(n+1−n)=∑n=1∞g(n+1)which converges since g is a positive, decreasing function. Hence the integral of option A is finite and less than infinity.Option A is less than option BCA and option ABC is infinite.

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-1 is the z-score corresponding to a raw score of 32 from a population.

To find which Z-score could correspond to a raw score of 32 from a population with a mean of 33, we can use the Z-score formula, which is:

Z = (X - μ) / σ

Where:

Z is the Z-score

X is the raw score

μ is the population mean

σ is the population standard deviation

First, we need to know the Z-score corresponding to the raw score of 33 (since that is the population mean). Then, we can use that Z-score to find the Z-score corresponding to the raw score of 32.

Here's how to solve the problem:

Z for a raw score of 33:

Z = (X - μ) / σ

Z = (33 - 33) / σ

Z = 0 / σ

Z = 0

This means that a raw score of 33 has a Z-score of 0.

Now we can use this Z-score to find the Z-score for a raw score of 32:

Z = (X - μ) / σ

0 = (32 - 33) / σ

0 = -1 / σ

σ = -1

This tells us that the Z-score corresponding to a raw score of 32 from a population with a mean of 33 is -1.

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"The given statement is False." The wheels on an automobile are not classified as a variable cost with respect to the volume of cars produced in an automobile assembly plant.

The statement is incorrect. The wheels on an automobile are not typically classified as a variable cost with respect to the volume of cars produced in an automobile assembly plant.

Variable costs are costs that vary in direct proportion to the level of production or activity. They increase or decrease as the volume of production changes.

Examples of variable costs in automobile manufacturing would include items such as raw materials, direct labor, and electricity costs.

On the other hand, the cost of wheels for an automobile assembly plant would typically be considered a fixed cost. Fixed costs are costs that do not vary with the level of production. These costs remain constant regardless of the number of cars produced.

Fixed costs in automobile manufacturing may include expenses like the purchase or lease of manufacturing equipment, facility rental, and salaries of administrative staff.

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Here's the LaTeX representation of the explanation:

To determine whether the series [tex]$\sum \frac{\arctan(10n)}{n^{1.3}}$[/tex] converges or diverges, we can use the limit comparison test.

Let's consider the series [tex]$\sum \frac{1}{n^{1.3}}$[/tex] . This is a [tex]$p$[/tex]-series with [tex]$p = 1.3$[/tex] , and we know that a [tex]$p$[/tex]-series converges if [tex]$p > 1$[/tex] and diverges if [tex]$p \leq 1$.[/tex]

Now, let's take the limit as [tex]$n$[/tex] approaches infinity of the ratio of the terms of the given series to the terms of the series  [tex]$\frac{1}{n^{1.3}}$[/tex]  :

[tex]\[\lim_{n \to \infty} \frac{\frac{\arctan(10n)}{n^{1.3}}}{\frac{1}{n^{1.3}}}\][/tex]

Simplifying this limit, we get:

[tex]\[\lim_{n \to \infty} \arctan(10n)\][/tex]

As [tex]$n$[/tex] approaches infinity, [tex]$\arctan(10n)$[/tex] also approaches infinity. Therefore, the limit is infinity.

Since the limit is not zero or a finite value, and the terms of the series do not approach zero, we can conclude that the given series diverges.

Therefore, the series [tex]$\sum \frac{\arctan(10n)}{n^{1.3}}$[/tex] diverges.

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Thus, we can use the value 6.24 years as a single point estimate for the mean time required to graduate with a bachelor's degree based on the available data.

To estimate the mean time required to graduate with a bachelor's degree based on the given data, we can use the sample mean as a point estimate.

The sample mean is calculated as the sum of all the individual times divided by the total number of graduates:

Sample Mean = (sum of all individual times) / (total number of graduates)

In this case, the given data states that the mean time required to graduate is 6.24 years for 4400 college graduates. Therefore, the sample mean is:

Sample Mean = 6.24 years

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a. The experiment fits into the Hypergeometric distribution.

b. The probability of finding no faulty parts is 0.0746 or 7.46%.

c. The probability of finding two faulty products is 0.4336 or 43.36%.

a. The experiment fits into the Hypergeometric distribution because it involves sampling without replacement from a finite population (100 products) that contains both defective (5 faulty products) and non-defective items (95 non-faulty products). The Hypergeometric distribution is appropriate when the sampling is done without replacement and the population size is small relative to the sample size.

b. To calculate the probability of finding no faulty parts, we use the Hypergeometric distribution formula:

P(X = 0) = (C(5, 0) * C(95, 5)) / C(100, 5)

where C(n, r) represents the combination function.

Calculating this formula, we find:

P(X = 0) = (1 * 1,221) / 75,287 = 0.0162 ≈ 0.0746 or 7.46%.

c. To calculate the probability of finding two faulty products, we again use the Hypergeometric distribution formula:

P(X = 2) = (C(5, 2) * C(95, 3)) / C(100, 5)

Calculating this formula, we find:

P(X = 2) = (10 * 14,070) / 75,287 = 0.2088 ≈ 0.4336 or 43.36%.

a. The experiment fits into the Hypergeometric distribution because it involves sampling without replacement from a finite population.

b. The probability of finding no faulty parts is approximately 0.0746 or 7.46%.

c. The probability of finding two faulty products is approximately 0.4336 or 43.36%.

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The given parabolic equation is y = 4x - x² and the point is (1, 3). We are to determine the slope of the tangent line at (1, 3) and then obtain an equation of the tangent line.  we must first calculate the derivative of the given equation.

We can do this by using the power rule of differentiation. The derivative of x² is 2x. So the derivative of y = 4x - x² is dy/dx = 4 - 2x.Since we want to find the slope of the tangent line at (1, 3), we need to substitute x = 1 into the equation we just obtained. dy/dx = 4 - 2x = 4 - 2(1) = 2. Therefore, the slope of the tangent line at (1, 3) is 2.We can now write the equation of the tangent line. We know the slope of the tangent line, m = 2, and we know the point (1, 3).

We can use the point-slope form of the equation of a line to obtain the equation of the tangent line. The point-slope form of the equation of a line is given as: y - y₁ = m(x - x₁)where m is the slope, (x₁, y₁) is a point on the line.Substituting in the values we have, we get:y - 3 = 2(x - 1)We can expand this equation to obtain the slope-intercept form of the equation of the tangent line:y = 2x + 1Therefore, the equation of the tangent line to the parabola y = 4x - x² at the point (1, 3) is y = 2x + 1.

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Answer:

4)a. A = π(3²) = 9π

b. h = 10

c. V = 9π(10) = 90π

Given the complex numbers x = 3 + 8i and y = 7 - i, we can match them with equivalent expressions. By substituting these values into the expressions.

we find that - x - y is equivalent to -8 - 41i, - 2x - 3y is equivalent to -15 + 19i, - 5x + y is equivalent to 58 + 106i, and - x - 2y is equivalent to -29 - 53i. These matches are determined by performing the respective operations on the complex numbers and simplifying the results.

Matching the equivalent expressions:

x - y matches -8 - 41i

2x - 3y matches -15 + 19i

5x + y matches 58 + 106i

x - 2y matches -29 - 53i

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The 25th, 50th, and 75th percentiles from the given list of 36 data 12 14 16 18 20 26 28 29 30 32 40 45 47 48 49 50 51 52 54 62 63 78 80 87 89 50th percentile= 28

The percentile is a statistical value that indicates the percentage of a distribution that is equal to or below it.

The steps to calculate percentiles:

Step 1: Arrange the data in ascending order

Step 2: Calculate the position of percentile (P) by using the formula P = (n * x) / 100, where n is the total number of data and x is the percentile.

Step 3: If P is a whole number, find the average of the two values at positions P and P + 1. If P is a decimal number, round up to the next whole number to find the position of the data value

Step 4: Find the value of the data at the Pth position. So the 50th percentile, also called the median, is the middle value of the dataset when it is arranged in ascending order.

From the given list of 36 data:12 14 16 18 20 26 28 29 30 32 40 45 47 48 49 50 51 52 54 62 63 78 80 87 89Since the total number of data is 36.

Find the 50th percentile, we will use the formula as follows:P = (n * x) / 100P = (36 * 50) / 100P = 18The 50th percentile   (or the median) is at the 18th position. Hence, the 50th percentile is 28.

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f(x) is not a valid PDF. Therefore, we can't compute E(Y) in this case.

Given X is a continuous random variable where X ∈ [3, 2] and f(2) = ? We have to find E(Y) where 1 ≤ X ≤ 2 and Y = (1/2)X otherwise Y = 0.

Since we don't have the PDF of the continuous random variable X, we can't compute the expected value E(Y) directly using the formula E(Y) = ∫yf(y)dy. However, we can use the Law of Total Probability to get the conditional PDF of Y given X and then use it to find E(Y).

So, let's find the conditional PDF f(Y|X) of Y given X. Since Y is a function of X, we have Y = g(X), where g(X) = (1/2)X for 1 ≤ X ≤ 2 and g(X) = 0 otherwise. Now, the conditional PDF f(Y|X) is given by: f(Y|X) = f(X,Y) / f(X)where f(X,Y) is the joint PDF of X and Y and f(X) is the marginal PDF of X.

The joint PDF f(X,Y) is given by: f(X,Y) = f(Y|X) * f(X)where f(Y|X) is given by: f(Y|X) = δ(Y - g(X)), where δ() is the Dirac delta function. Thus, f(X,Y) = δ(Y - g(X)) * f(X) Now, we need to find f(X). Since X is a continuous random variable, we have: f(X) = ∫f(X,Y)dy = ∫δ(Y - g(X))dy

Using the property of the Dirac delta function, we get: f(X) = δ(Y - g(X))|y=g(X) = δ(Y - (1/2)X) Therefore, f(Y|X) = δ(Y - g(X)) / δ(Y - (1/2)X) for 1 ≤ X ≤ 2 and f(Y|X) = 0 otherwise.

Now, we can use the formula for the conditional expected value to get E(Y|X = x):E(Y|X = x) = ∫yf(y|x)dy= ∫y * δ(Y - g(x)) / δ(Y - (1/2)x) dy= g(x) = (1/2)x for 1 ≤ x ≤ 2and E(Y|X = x) = 0 otherwise. Then, we can use the formula for the Law of Total Probability to get E(Y):E(Y) = ∫E(Y|X = x)f(x)dx = ∫(1/2)x * f(x) dx for 1 ≤ x ≤ 2and E(Y) = 0 otherwise.

Since we don't have the PDF of X, we can't compute E(Y) directly. However, we can use the fact that the integral of a PDF over its domain is equal to 1.

Therefore, we have:1 = ∫f(x)dx from which we can solve for f(x):f(x) = 1 / ∫dx from which we get: f(x) = 1 / [2 - 3] = 1/-1 = -1

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To calculate the volume of a right circular cylinder, we can use the formula:

Volume = π * r^2 * h

Where:

π is the mathematical constant pi (approximately 3.14159)

r is the radius of the base of the cylinder (half the diameter)

h is the height of the cylinder

Given:

Base diameter = 6 m

Radius (r) = (base diameter) / 2 = 6 m / 2 = 3 m

Height (h) = 5 m

Substituting the values into the formula, we have:

Volume = π * (3 m)^2 * 5 m

= π * 9 m^2 * 5 m

= π * 45 m^3

Therefore, the volume of the cylinder is 45π cubic meters.

the volume of the right circular cylinder with a base diameter of 6 m and a height of 5 m is 45π m³ By using formula of

V = πr²h

The volume of a right circular cylinder with a base diameter of 6 m and a height of 5 m is given by:V = πr²hwhere r is the radius of the cylinder and h is the height of the cylinder. Since the base diameter of the cylinder is given as 6 m, we can find the radius by dividing it by 2:r = d/2 = 6/2 = 3 m Therefore, the volume of the cylinder is:V = π(3 m)²(5 m)V = π(9 m²)(5 m)V = 45π m³Therefore, the volume of the right circular cylinder with a base diameter of 6 m and a height of 5 m is 45π m³.

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The probability that among 5 adults with smartphones, all 5 use them in meetings or classes is 0.00221 (rounded to five decimal places).Therefore, the answer is 0.00221.

The required answer to the given question is as follows: Given Data: It is given that when adults with smartphones are randomly selected, 44% use them in meetings or classes.

We have to calculate the probability that among 5 adults with smartphones, all 5 use them in meetings or classes.

Concept Used: Here we use the concept of probability of Independent events which states that if the probability of occurrence of one event does not affect the probability of occurrence of the other event then the events are known as Independent events.

Formula Used: The formula used for probability of Independent events is: P(A and B) = P(A) x P(B) Where, P(A and B) represents the probability of occurrence of both A and BP(A) represents the probability of occurrence of A. P(B) represents the probability of occurrence of B.

Calculation: Given that when adults with smartphones are randomly selected, 44% use them in meetings or classes. The probability that a person use the smartphones in meeting or class is 44/100 = 0.44So, the probability that a person does not use the smartphones in meeting or class is 1 - 0.44 = 0.56

Now, we need to find the probability that among 5 adults with smartphones, all 5 use them in meetings or classes. So, we can say that these are Independent events. Now, P(A and B and C and D and E) = P(A) x P(B) x P(C) x P(D) x P(E) = 0.44 x 0.44 x 0.44 x 0.44 x 0.44 = 0.00221

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The height of the tree, considering the similar triangles in this problem, is given as follows:

32.5 feet.

Two triangles are defined as similar triangles when they share these two features listed as follows:

The proportional relationship for the side lengths in this problem is given as follows:

25/5 = h/6.5

5 = h/6.5.

Hence the height of the tree is obtained applying cross multiplication as follows:

h = 6.5 x 5

h = 32.5 feet.

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The standard deviation of the given probability distribution is $23.45.

The correct answer is option B.

The standard deviation of the given probability distribution is determined as follows:

Calculate the expected value (mean) of the distribution:

Expected Value = (Probability1 * Value1) + (Probability2 * Value2) + (Probability3 * Value3)

Expected Value = (0.25 * (-30)) + (0.50 * 20) + (0.25 * 30)

Expected Value = -7.50 + 10 + 7.50

Expected Value = 10

The squared deviation for each value:

Squared Deviation1 = (Value1 - Expected Value)² * Probability1

Squared Deviation2 = (Value2 - Expected Value)² * Probability2

Squared Deviation3 = (Value3 - Expected Value)² * Probability3

Squared Deviation1 = (-30 - 10)² * 0.25 = 1600 * 0.25 = 400

Squared Deviation2 = (20 - 10)² * 0.50 = 100 * 0.50 = 50

Squared Deviation3 = (30 - 10)² * 0.25 = 400 * 0.25 = 100

Variance = Squared Deviation1 + Squared Deviation2 + Squared Deviation3

Variance = 400 + 50 + 100 = 550

Standard Deviation = √Variance

Standard Deviation = √550

Now, calculating the square root of 550 gives us an approximate value of 23.45.

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The given differential equation is: y'' − 14y' + 40y = 0. To find the two values of k for which y(x) = ekx is a solution of the differential equation, we first differentiate y(x) twice. We get y'(x) = ekxk and y''(x) = ekxk2. Now we substitute these values in the differential equation and get;ekxk2 − 14ekxk + 40ekxk = 0ekxk [k2 − 14k + 40] = 0k2 − 14k + 40 = 0Solving this quadratic equation gives us;k = 7 ± √9.

The two values of k are; Smaller value = 7 − √9Larger value = 7 + √9Now we need to simplify this further. We know that √9 = 3Therefore,Smaller value = 7 − 3 = 4Larger value = 7 + 3 = 10Therefore, the two values of k for which y(x) = ekx is a solution of the differential equation y'' − 14y' + 40y = 0 are 4 and 10. The smaller value is 4 and the larger value is 10.

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Given the function y(t) = 5sin 4t + 3cos 4t. We need to rewrite it in terms of a cosine term only and sine term only.a) a cosine term only We can use the formula of sin (a + b) = sin a cos b + cos a sin b.

Using this formula, we can write, y(t) = 5sin 4t + 3cos 4t = √34 [√(5/17)sin 4t + √(12/17)cos 4t]We know, cos (90° - θ) = sin θ and sin (90° - θ) = cos θThus, we can rewrite the above equation as,y(t) = √34 [cos (90° - 4t) √(5/17) + sin (90° - 4t) √(12/17)]Thus, y(t) = √34 cos (4t - 0.37)b) a sine term only We can use the formula of cos (a + b) = cos a cos b - sin a sin b.

Using this formula, we can write, y(t) = 5sin 4t + 3cos 4t = √34 [√(12/17)sin 4t - √(5/17)cos 4t]We know, cos (90° - θ) = sin θ and sin (90° - θ) = cos θThus, we can rewrite the above equation as,y(t) = √34 [sin (4t + 1.18) √(12/17)]Thus, y(t) = √408/17 sin (4t + 1.18)The frequency of both sine and cosine functions is equal to 4 rad/s The amplitude of sine function = √408/17 = 2.73The amplitude of cosine function = √34 = 5.83The phase angle of cosine function = 0.37 rad The phase angle of sine function = 1.18 rad.

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Therefore, the area of the region R is A = -10.5/3 square units.

To find the area of the region bounded by the functions[tex]f(x) = x^2 - 3x - 3[/tex] and g(x) = -2x + 3, we need to determine the points of intersection between the two functions.

Setting f(x) equal to g(x), we have:

[tex]x^2 - 3x - 3 = -2x + 3[/tex]

Rearranging the equation and simplifying:

[tex]x^2 - x - 6 = 0[/tex]

Factoring the quadratic equation:

(x - 3)(x + 2) = 0

This gives us two solutions: x = 3 and x = -2.

To find the area, we integrate the difference between the two functions over the interval [x = -2, x = 3]:

A = ∫[from -2 to 3] (f(x) - g(x)) dx

Substituting the functions:

A = ∫[from -2 to 3] [tex]((x^2 - 3x - 3) - (-2x + 3)) dx[/tex]

Simplifying:

A = ∫[from -2 to 3] [tex](x^2 + x - 6) dx[/tex]

Integrating the polynomial:

A =[tex][(1/3)x^3 + (1/2)x^2 - 6x][/tex] [from -2 to 3]

Evaluating the integral:

[tex]A = [(1/3)(3^3) + (1/2)(3^2) - 6(3)] - [(1/3)(-2^3) + (1/2)(-2^2) - 6(-2)][/tex]

Simplifying further:

A = [(1/3)(27) + (1/2)(9) - 18] - [(1/3)(-8) + (1/2)(4) + 12]

A = [9 + 4.5 - 18] - [-8/3 - 2 + 12]

A = 4.5 - (8/3) + 2 - 12

A = -3.5 - (8/3)

A = -10.5/3

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The given function is:y = −3 + (1/cos(xx - ²))³ = - 3 + (1/cos(x- ²))³The function is shifted 2 units to the right.Phase shift, P = 2 Final answer:Period: 2πAmplitude: 1 Phase shift: 2

Given function is

y = −3 + (1/cos(xx - ²))³Period:

Period is the distance after which the function will repeat itself. For finding the period of the given function, use the formula:

T = 2π/b

Where T = period and b is the coefficient of x Here the coefficient of x is 1 Period,

T = 2π/1 = 2πAmplitude:

Amplitude of the given function can be determined by observing the graph of the function or it can be calculated using the formula

A = |1/b|

Here b is the coefficient of cosx, which is 1.Amplitude, A = |1/b| = 1Phase Shift:The general form of cosine function is:

y = A cos (bx - c) + d

Here A is the amplitude, b is the coefficient of x, c is the phase shift and d is the vertical shift. Phase shift is the horizontal shift of the graph of the given function.The given function is:

y = −3 + (1/cos(xx - ²))³ = - 3 + (1/cos(x- ²))³

The function is shifted 2 units to the right.Phase shift, P = 2 Final answer:

Period: 2πAmplitude: 1 Phase shift: 2

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The probability of the shuttle being left with no computer control systems on a mission is 0.000001.

The probability of failure for any one system on any one mission is known to be 0.01.

Since a failure of one system is independent of a failure of another system, the probability that the shuttle is left with no computer control system on a mission is 0.01 × 0.01 × 0.01 = 0.000001, or 1 in 1,000,000.

This is because the probability of three independent events occurring is the product of the individual probabilities.

Therefore, the probability of the shuttle being left with no computer control systems on a mission is 0.000001.

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a.) The area of the shaded face would be =54cm²

b.) The number of rectangular faces that the prism has =3

c.) The total area of the rectangular faces would be=162cm².

To calculate the area of the shaded face, the formula that should be used = length×width.

where;

Length = 9cm

width = 6cm

Area = 9×6 = 54cm²

The total number of rectangular faces = 3

The total area of these rectangular face would be area of one rectangular face multiplied by 3.

That is;

54×3 = 162cm²

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