What’s the unit rate of 144 pages in 3 minutes ?

The proportion is solved and the number of men , women and children are 600 men, 1080 women, and 1200 children respectively

Given data ,

Let the proportion be represented as A

Now , the value of A is

Ratio of men : women : children = 5 : 9 : 10

And , Men:Women:Children = 5x : 9x : 10x

It is also given that there are 480 more women than men.

Since the ratio of men to women is 5:9, we can set up the equation:

9x - 5x = 480

Simplifying, we get:

4x = 480

Dividing both sides by 4, we get:

x = 120

Now we can substitute the value of x back into the ratio to find the actual number of men, women, and children.

(a) Men = 5x = 5 x 120 = 600

(b) Women = 9x = 9 x 120 = 1080

(c) Children = 10x = 10 x 120 = 1200

Hence , there are 600 men, 1080 women, and 1200 children

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The value of y in the parallelogram is 37

A parallelogram is a quadrilateral with two pairs of parallel sides.These are some of properties of a parallelogram.

1. The opposite sides of a parallelogram are equal in length,

2. The opposite angles are equal in measure.

3. The sum of adjascent angles is 180°

This means that;

4y - 42+ 2y = 180°

collecting like terms

4y +2y = 180+42

6y = 222

divide both sides by 6

y = 222/6

y = 37

therefore the value of y Is 37

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He would fail to reject the null hypothesis and conclude that there is not enough evidence to support the claim.

What is null hypothesis?

The null hypothesis is:

H0: p <= 0.58

The alternative hypothesis is:

Ha: p > 0.58

Where p represents the true proportion of adults who take a daily multivitamin.

The medical adviser could conduct a hypothesis test using a significance level (alpha) of 0.05. He would need to collect a sample of adults and determine the proportion who take a daily multivitamin. He could then calculate the test statistic, which in this case would be a one-sample z-test, using the following formula:

z = (p - p0) / sqrt(p0(1 - p0) / n)

Where p is the sample proportion, p0 is the hypothesized proportion under the null hypothesis, and n is the sample size.

If the test statistic falls in the rejection region (i.e. if the p-value is less than the significance level), the medical adviser would reject the null hypothesis and conclude that there is evidence to support the claim that the proportion of adults who take a daily multivitamin is more than 0.58. If the test statistic does not fall in the rejection region, he would fail to reject the null hypothesis and conclude that there is not enough evidence to support the claim.

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Based on the number of doctors at the hospital who obtained results as extreme or more extreme than the results from the study,  since 13 out of 20 samples have more than 7 adults who take a daily multivitamin, there is NOT evidence to suggest that the proportion of adults who take a multivitamin is more than 58%. the correct answer is b.

To determine whether there is evidence to suggest that more than 58% of adults take a daily multivitamin, we need to analyze the results from the 20 samples taken by the doctors.

The statement mentions that 13 out of the 20 samples have more than 7 adults who take a daily multivitamin. This means that in these 13 samples, the proportion of adults taking a multivitamin is higher than 7/12, which is equivalent to 58%.

To determine whether this difference is statistically significant, we typically perform a hypothesis test. The null hypothesis (H0) in this case is that the proportion of adults taking a multivitamin is 58% or less (p ≤ 0.58). The alternative hypothesis (Ha) is that the proportion is more than 58% (p > 0.58).

If the observed results are significantly different from what would be expected under the null hypothesis, we reject the null hypothesis and conclude that there is evidence to support the alternative hypothesis.

Since 13 out of 20 samples have more than 7 adults who take a daily multivitamin, there is NOT evidence to suggest that the proportion of adults who take a multivitamin is more than 58% because it acknowledges that the observed results are not significantly different from the original study. The correct answer is B.

In hypothesis testing, we typically set a significance level (e.g., 0.05) to determine if the evidence is statistically significant. If the p-value (the probability of obtaining results as extreme or more extreme than the observed results) is less than the significance level, we reject the null hypothesis.

Your question is incomplete but probably your full question was attached below

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The probability P(z > -2.75) using the standard normal distribution is approximately 0.9970.

To find the indicated probability using the standard normal distribution, we need to use a z-table.

The standard normal distribution has a mean of 0 and a standard deviation of 1. The z-score represents the number of standard deviations away from the mean.

To find P(z>-2.75), we need to find the area under the standard normal distribution curve to the right of -2.75.

Using a z-table, we can find that the area to the right of -2.75 is 0.9970.

Therefore, P(z>-2.75) = 0.9970 or approximately 0.997.

This means that there is a 99.7% probability that a randomly selected value from the standard normal distribution will be greater than -2.75 standard deviations from the mean.

To find the indicated probability P(z > -2.75) using the standard normal distribution, follow these steps:

1. Identify the z-score: In this case, the z-score is -2.75.

2. Use the standard normal distribution table or a calculator with a built-in z-table function to find the area to the left of the z-score.

3. Since we need to find the probability P(z > -2.75), we'll subtract the area to the left of the z-score from 1 (the total probability).

Step-by-step calculation:

1. z-score = -2.75

2. Look up the area to the left of the z-score in the standard normal distribution table or using a calculator. For z = -2.75, the area to the left is approximately 0.0030.

3. To find the probability P(z > -2.75), subtract the area to the left from 1:

  P(z > -2.75) = 1 - 0.0030 = 0.9970

So, the probability P(z > -2.75) using the standard normal distribution is approximately 0.9970.

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A Single transformation maps triangle ABC onto triangle DEF.

Given  that Reflection the single  transformation maps ABC onto A'B'C' is reflection .

The rigid transformations can map triangle Δ ABC onto triangle Δ DEF is reflection then translation .

The rigid transformations that will map Δ ABC to Δ DEF is rotation then translation .

However pair of triangles can be proven congruent by the HL theorem is rotation then translation .

Thus, rigid transformation S can map Triangle ABC onto Triangle DEF is reflection then rotation .

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The value corresponding to the percentile rank of 30% is found in the 10-14 class interval, as its cumulative percentage is 30%. The approximate percentile rank of X = 36 is 90%, as the cumulative percentage for the class interval of 35-39 is 90%.

To create a relative frequency table of raw score values, we first need to create class intervals with a width of 5. The class intervals are: 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, and 35-39.

| Raw Scores | Frequency | Cumulative Frequency | Cumulative Percentage |
|------------|-----------|----------------------|-----------------------|
| 5-9        | 0         | 0                    | 0%                    |
| 10-14      | 2         | 2                    | 20%                   |
| 15-19      | 1         | 3                    | 30%                   |
| 20-24      | 1         | 4                    | 40%                   |
| 25-29      | 3         | 7                    | 70%                   |
| 30-34      | 1         | 8                    | 80%                   |
| 35-39      | 1         | 9                    | 90%                   |

To draw a relative frequency histogram, we plot the class intervals on the x-axis and the relative frequencies on the y-axis. The shape of the distribution appears to be positively skewed.

The value corresponding to the percentile rank of 30% can be found by looking at the cumulative percentage column in the frequency table. The cumulative percentage at the end of the 2nd class interval is 20%, so the value corresponding to the 30th percentile is somewhere between 10 and 14. Using linear interpolation, we can estimate that the value corresponding to the 30th percentile is approximately 11.6.

To find the approximate percentile rank of X = 36, we can look at the raw scores column in the frequency table and find the class interval that contains 36, which is 35-39. The cumulative frequency at the end of this interval is 9, and the total number of scores is 10. Therefore, the percentile rank of X = 36 is approximately 90%.
To create a relative frequency table of raw score values using class intervals with a width of 5, we can categorize the data as follows:

Class Intervals (Raw Scores): 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44

Frequency: 2, 1, 2, 3, 0, 1, 1

Cumulative Frequency: 2, 3, 5, 8, 8, 9, 10

Cumulative Percentage: 20%, 30%, 50%, 80%, 80%, 90%, 100%

Next, draw a relative frequency histogram with the class intervals on the x-axis and the frequency on the y-axis. The shape of the distribution is positively skewed, as most data points are on the lower end of the scale, with a few points in the higher range.

The value corresponding to the percentile rank of 30% is found in the 10-14 class interval, as its cumulative percentage is 30%.

The approximate percentile rank of X = 36 is 90%, as the cumulative percentage for the class interval of 35-39 is 90%.

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To calculate the probability of getting at most five successes, we can add up the probabilities of getting 0, 1, 2, 3, 4, or 5 successes

Sure, here are the expressions that can be used to compute each of the probabilities:

a) p(exactly five successes) = (7 choose 5) * p^5 * q^2
This expression uses the binomial coefficient (7 choose 5), which represents the number of ways to choose 5 successes out of 7 trials. We then multiply this by the probability of getting 5 successes (p^5) and the probability of getting 2 failures (q^2).

b) p(at least five successes) = p(five successes) + p(six successes) + p(seven successes)
To calculate the probability of getting at least five successes, we can add up the probabilities of getting exactly 5, 6, or 7 successes. We can use the expression from part a to calculate each of these probabilities.

c) p(at most five successes) = p(zero successes) + p(one success) + p(two successes) + p(three successes) + p(four successes) + p(five successes)
. We can again use the expression from part a to calculate each of these probabilities.

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The two numbers whose difference is 132 and whose product is a minimum are 66 and 198.

Let x and y be the two numbers. We are given that their difference is 132, so we can write:

y - x = 132

We want to minimize their product, which is given by:

P = xy

We can solve for one of the variables in terms of the other using the first equation:

y = x + 132

Substituting this expression for y in the equation for P, we get:

P = x(x + 132)

Expanding this expression and simplifying, we get:

P = x^2 + 132x

To find the minimum value of P, we can take the derivative of P with respect to x and set it equal to zero:

dP/dx = 2x + 132 = 0

Solving for x, we get:

x = -66

Since we want the two numbers to be positive, we take the absolute value of x and add 132 to get y:

x = 66

y = x + 132 = 198

Therefore, the two numbers whose difference is 132 and whose product is a minimum are 66 and 198.

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The two statements that also describe features of a binomial experiment are: "The random variable counts the number of successes in a fixed number of trials" and "The probability of success stays the same for each trial."

Binomial experiments also have independent trials, which means that the outcome of one trial does not affect the outcome of another trial. Additionally, binomial experiments can only have two possible outcomes - success or failure - for each trial. The distribution of a binomial experiment is not always asymmetric in shape, as it can be symmetrical or skewed depending on the parameters of the experiment.

Finally, subsequent trials in a binomial experiment are not conditional probabilities, as each trial is independent and the probability of success remains constant.
Based on your question and the terms provided, the two statements that describe features of a binomial experiment are:

1. The random variable counts the number of successes in a fixed number of trials.
2. The probability of success stays the same for each trial.

A binomial experiment has independent trials with a constant probability of success, and it focuses on counting the number of successes within a specific number of trials.

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Answer: That is correct. In a binary tree, each node has at most two child nodes (hence the name "binary"), and the height of the tree is the length of the longest path from the root node to a leaf node.

If the tree has n leaves, then the number of nodes in the tree is at most 2n-1 (since each node can have at most 2 child nodes and there is only one root node), and the height of the tree is log2(2n) = log2n + 1 (since there are 2n nodes at level log2n, and we need to add 1 for the root node).

However, if the tree is not perfectly balanced, it is possible for the height to be slightly larger than log2n + 1. Nonetheless, log2n is still a tight upper bound on the height of a binary tree with n leaves.

Based on the given information, we can expect the p-value for a two-sided test using this data to be smaller than 0.05. This is because the 95% confidence interval for the mean does not contain 0, which indicates that there is a statistically significant difference between the yield of silver queen and country gentlemen.

A p-value is a measure of the evidence against the null hypothesis (which in this case would be that there is no difference between the two corn varieties). The smaller the p-value, the stronger the evidence against the null hypothesis and the more likely we are to reject it. In general, if the p-value is smaller than the chosen significance level (usually 0.05 or 0.01), we reject the null hypothesis and conclude that there is a significant difference between the two groups being compared.

Therefore, in this case, we can expect the p-value to be smaller than 0.05 and conclude that there is a significant difference in the yield of silver queen and country gentlemen.

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Using a chi-square table or calculator, we find that this probability is approximately 0.025.

To answer this question, we need to use the chi-square distribution, which is used to find probabilities for sample variances.

a) For a random sample of size n = 16, we use the formula:
Chi-Square = (n-1) * sample variance / population variance
Chi-Square = 15 * 7.442 / 5
Chi-Square = 22.326

The probability of a sample variance being greater than or equal to 7.442 is the same as the probability of a chi-square value of 22.326 or more. Using a chi-square table or calculator, we find that this probability is approximately 0.05.

The probability of a sample variance being less than or equal to 2.56 is the same as the probability of a chi-square value of 7.692 or less. Using a chi-square table or calculator, we find that this probability is approximately 0.025.

b) For a random sample of size n = 30, we use the formula:
Chi-Square = (n-1) * sample variance / population variance
Chi-Square = 29 * 7.442 / 5
Chi-Square = 41.256

The probability of a sample variance being greater than or equal to 7.442 is the same as the probability of a chi-square value of 41.256 or more. Using a chi-square table or calculator, we find that this probability is approximately 0.005.

The probability of a sample variance being less than or equal to 2.56 is the same as the probability of a chi-square value of 13.767 or less. Using a chi-square table or calculator, we find that this probability is approximately 0.025.

c) Comparing the probabilities in parts (a) and (b) for the sample variance being greater than or equal to 7.442, we see that the probability decreases with increased sample size. This is because as the sample size increases, the sample variance is more likely to be closer to the population variance, resulting in a smaller chi-square value.

d) Comparing the probabilities in parts (a) and (b) for the sample variance being less than or equal to 2.56, we see that the probability remains the same regardless of sample size. This is because a smaller sample variance is always more likely than a larger one, regardless of the sample size.

In a normal population with a known mean (µ) of 75 and variance (σ²) of 5, we want to find the approximate probability that the sample variance is greater than or equal to 7.442 and less than or equal to 2.56 for random samples of size n=16 and n=30.

a) For a random sample of size n=16, the chi-square distribution with degrees of freedom (df) = n-1 = 15 is used to calculate the probability.

b) For a random sample of size n=30, the chi-square distribution with degrees of freedom (df) = n-1 = 29 is used to calculate the probability.

c) Comparing the answers to parts (a) and (b) for the approximate probability that the sample variance is greater than or equal to 7.44, it is observed that the tail probability may either increase or decrease with an increased sample size, depending on the underlying distribution of the population.

d) Comparing the answers to parts (a) and (b) for the approximate probability that the sample variance is less than or equal to 2.56, it is observed that the tail probability may either increase or decrease with an increased sample size, again depending on the underlying distribution of the population.

The actual probabilities will depend on the specific calculations made using the chi-square distribution and the given parameters.

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Answer:

[tex]a=\dfrac{1}{2},\;\;b=\dfrac{1}{2}[/tex]

Step-by-step explanation:

A function f is continuous at x = a when:

    [tex]\bullet\quad\textsf{$f(a)$\;is\;d\:\!efined.}[/tex]

    [tex]\bullet\quad\textsf{$\displaystyle \lim_{x \to a} f(x)$\;exists.}[/tex]

    [tex]\bullet\quad\displaystyle \lim_{x \to a} f(x) = f(a)[/tex]

To ensure that f(x) is continuous at x = 2 and x = 3, we need to make sure that the limit of f(x) as x approaches 2 from the left is equal to the limit of f(x) as x approaches 2 from the right, and similarly for x = 3.

First, factor the numerator and simplify the rational function:

[tex]f(x)=\dfrac{x^2-4}{x-2}=\dfrac{(x+2)(x-2)}{x-2}=x+2[/tex]

As x approaches 2 from the left, x < 2. Therefore, to find the limit as x approaches 2 from the left, substitute x = 2 into the first sub-function:

[tex]\displaystyle \lim_{x \to 2^{-}} f(x)=2+2=4[/tex]

As x approaches 2 from the right, x > 2. Therefore, to find the limit as x approaches 2 from the right, substitute x = 2 into the second sub-function:

[tex]\begin{aligned}\displaystyle \lim_{x \to 2^{+}} f(x)&=a(2)^2-b(2)+3\\&=4a-2b+3\end{aligned}[/tex]

To ensure continuity at x = 2, equate the limits:

[tex]4=4a - 2b + 3[/tex]

Solve for b in terms of a:

[tex]\begin{aligned}4&=4a - 2b + 3&\\ 2b+4&=4a+3\\2b&=4a-1\\b&=2a-\dfrac{1}{2}\end{aligned}[/tex]

Now, we need to find a and b so that f(x) is continuous at x = 3.

As x approaches 3 from the left, x < 3. Therefore, to find the limit as x approaches 3 from the left, substitute x = 3 into the second sub-function:

[tex]\begin{aligned}\displaystyle \lim_{x \to 3^{-}} f(x)&=a(3)^2 - b(3) + 3\\&= 9a - 3b + 3\end{aligned}[/tex]

As x approaches 3 from the right, x > 3. Therefore, to find the limit as x approaches 3 from the right, substitute x = 3 into the third sub-function:

[tex]\begin{aligned}\displaystyle \lim_{x \to 3^{+}} f(x)&= 2(3) - a + b\\&= 6 - a + b\end{aligned}[/tex]

To ensure continuity at x = 3, equate the limits:

[tex]9a - 3b + 3 = 6-a+b[/tex]

Solve for b in terms of a:

[tex]\begin{aligned}9a - 3b + 3 &= 6-a+b\\10a-3b+3&=6+b\\10a+3&=6+4b\\10a-3&=4b\\4b&=10a-3\\b&=\dfrac{5}{2}a-\dfrac{3}{4}\end{aligned}[/tex]

Substitute this expression for b into the equation obtained from continuity at x = 2:

[tex]\dfrac{5}{2}a-\dfrac{3}{4}=2a-\dfrac{1}{2}[/tex]

Solve for a:

[tex]\begin{aligned}\dfrac{5}{2}a-\dfrac{3}{4}&=2a-\dfrac{1}{2}\\\\\dfrac{1}{2}a-\dfrac{3}{4}&=-\dfrac{1}{2}\\\\\dfrac{1}{2}a&=\dfrac{1}{4}\\\\a&=\dfrac{1}{2} \end{aligned}[/tex]

Substitute the found value of a into the expression for b and solve for b:

[tex]\begin{aligned}b&=2\left(\dfrac{1}{2}\right)-\dfrac{1}{2}\\b&=1-\dfrac{1}{2}\\b&=\dfrac{1}{2}\end{aligned}[/tex]

Therefore, the values of a and b that make f(x) continuous at all points are:

[tex]a=\dfrac{1}{2},\;\;b=\dfrac{1}{2}[/tex]

So the function f(x) is:

[tex]f(x)=\begin{cases} \dfrac{x^2-4}{x-2}&\text{if}\;\;x < 2\\\\\dfrac{1}{2}x^2-\dfrac{1}{2}x+3\quad&\text{if}\;\;2 \leq x < 3\\\\2x&\text{if}\;\;x \geq 3\end{cases}[/tex]

The inequality showing the maximum number of hours left for her to work that week is: x ≤ 12 and this means she can work a maximum of 12 remaining hours for that week

Inequalities could either be in these forms:

Less than

Greater than

Greater than or equal to

Less than or equal to

We are given the parameters:

Maximum number of hours per week = 38 hours

Number of hours she has worked so far this week is 26

If the remaining number of hours she can work is x, then we have the inequality as:

x + 26 ≤ 38

Subtract 26 from both sides to get:

x ≤ 12

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X - Y and X + Y are uncorrelated. To show that X - Y and X + Y are uncorrelated, we need to show that their covariance is zero.

The covariance between X - Y and X + Y is given by:

[tex]Cov(X - Y, X + Y) = E[(X - Y)(X + Y)] - E[X - Y]E[X + Y][/tex]

Expanding the first term:

Cov(X - Y, X + Y) = E[X^2 - Y^2] - E[X - Y]E[X + Y]

Using the fact that X and Y have the same variance, we have:

[tex]E[X^2 - Y^2] = E[(X - Y)(X + Y)] = E[X^2] - E[Y^2][/tex]

And since X and Y have the same variance[tex], E[X^2] = E[Y^2][/tex], so we can simplify:

[tex]E[X^2 - Y^2] = 0[/tex]
Next, we can expand the second term:

E[X - Y]E[X + Y] = (E[X] - E[Y])(E[X] + E[Y])

Since X and Y have the same variance, we have E[X] = E[Y], so:

E[X - Y]E[X + Y] = (E[X] - E[X])(E[X] + E[X]) = 0

Putting it all together:

Cov(X - Y, X + Y) = 0 - 0 = 0

Therefore, X - Y and X + Y are uncorrelated.

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Answer:

Step-by-step explanation:

the standard equation for the circle is:

(x-a)²+(y-b)² = r²

the center is : A(a,b)    and ridus r

you have : a= 2 and b= - 3   r²= (3-2)²+(5+3)²= 1+ 64

r² 65    

the standard equation for the circle is:

(x-2)²+(y²+3 )=65

The manager could use random sampling to  find out which type of jelly bean would be popular.

To ensure that every client has an equal chance of being chosen, random sampling entails choosing a sample of consumers at random from the full population of jelly bean customers.

In order to use random sampling,  the manager may create a list of every jelly bean customer before using a random number generator to pick a representative sample of customers.

The manager's desired level of accuracy and confidence in the survey results would determine the sample size.

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The total number of people who preferred swimming at the beach is: 24

In survey sampling, the term probability of selection is one that refers to the chance (i.e. the probability from 0 to 1) that a member (element) of a population can be chosen for a given survey.

We are told that two out of 10 people preferred swimming at the beach. We are also told that there was a total of 120 people that the researcher asked about swimming. Thus:

Fraction of people that prefer swimming = 2/10 = 0.2

Thus, number of people that prefer swimming in a sample of 120 people is: 0.2 * 120 = 24 people

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The statement is true. The mean one-way commute time for the 30 largest U.S. cities is 25.8 minutes, and New York City has the longest mean travel time at 37.5 minutes, with a normal probability distribution and a standard deviation of 6.5 minutes.

True. The question states that among the 30 largest U.S. cities, the mean one-way commute time to work is 25.8 minutes. This means that on average, the commute time for these 30 cities is 25.8 minutes. However, the question also states that the longest one-way travel time is in New York City, where the mean time is 37.5 minutes. This means that New York City has a longer commute time compared to the other 29 cities.

Assuming the distribution of travel times in New York City follows the normal probability distribution and the standard deviation is 6.5 minutes, it is possible to calculate the probability of having a certain commute time. For example, the probability of having a commute time between 30 and 40 minutes can be calculated using the normal distribution formula.

Overall, it is true that among the 30 largest U.S. cities, the mean one-way commute time to work is 25.8 minutes and the longest one-way travel time is in New York City, where the mean time is 37.5 minutes.

True. Among the 30 largest U.S. cities, the mean one-way commute time to work is 25.8 minutes. New York City, known for its extensive public transportation system and traffic congestion, has the longest one-way travel time with a mean time of 37.5 minutes. Since the distribution of travel times in New York City follows the normal probability distribution, we can use this information to understand the variation in commute times for its residents.

The standard deviation for this distribution is 6.5 minutes, which indicates how spread out the travel times are from the mean. A smaller standard deviation would mean that most commute times are closer to the mean, while a larger standard deviation signifies that the data is more spread out, with some individuals having much shorter or longer commutes than the average.

In conclusion, the statement is true. The mean one-way commute time for the 30 largest U.S. cities is 25.8 minutes, and New York City has the longest mean travel time at 37.5 minutes, with a normal probability distribution and a standard deviation of 6.5 minutes.

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True, a box can be held in position by a cable along a frictionless incline.

When the tension force in the cable is equal to the component of the gravitational force rate acting parallel to the incline, the box will remain in a stationary position.

force which prevents one solid item from rolling or slipping over another. Although frictional forces can be advantageous, such as the traction required to walk without slipping, they can provide a significant amount of resistance to motion.

What are the 4 different forms of friction?

It opposes the sliding motion of both layers of fluid and solid matter. Friction comes in four flavors: flowing, rolling, sliding, and static.

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Answer:

The boat traveled at 10 miles per hour for 2 hours and at 30 miles per hour for 4 hours.

Step-by-step explanation:

Let x be the time (in hours) that the boat traveled at 10 miles per hour, and let y be the time (in hours) that it traveled at 30 miles per hour.

We know that:

x + y = 6 (the total time the boat traveled)

We also know that the boat traveled a total distance of 140 miles, which can be expressed as:

10x + 30y = 140

We can use the first equation to solve for one of the variables in terms of the other:

y = 6 - x

Substituting this expression for y into the second equation, we get:

10x + 30(6 - x) = 140

Simplifying and solving for x, we get:

10x + 180 - 30x = 140

-20x = -40

x = 2

Therefore, the boat traveled at 10 miles per hour for 2 hours and at 30 miles per hour for 4 hours.

The sequence for the nth term = 5n+6 is: 11, 16, 21, 26, 31,...

The sequence for the nth term = 5n+6 is:

n = 1, the nth term is 5(1)+6 = 11

n = 2, the nth term is 5(2)+6 = 16

n = 3, the nth term is 5(3)+6 = 21

n = 4, the nth term is 5(4)+6 = 26

n = 5, the nth term is 5(5)+6 = 31

and so on.

Therefore, the sequence for the nth term = 5n+6 is: 11, 16, 21, 26, 31,...

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Answer: The answer is 3

Step-by-step explanation: I had the same question.

The term that represents the typical average speed is 30/3s

From the question, we have the following parameters that can be used in our computation:

Expression = 20/s + 30/3s

We understand that

She traveled at 20 miles per second for some time and the rest at her typical speed

This means that

Typical speed = 30/3s

Hence, the term of the average speed is 30/3s

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This statistical test is appropriate because it allows for the examination of the effects of two independent variables (Type of Organization and Gender) on a continuous dependent variable (average donations).

The researcher should use a two-way ANOVA (analysis of variance) test to assess the difference in charitable donations based on both the type of organization and gender. This will allow the researcher to determine whether  To analyze the difference in charitable donations based on the Type of Organization and Gender, the researcher should use a Two-Way ANOVA (Analysis of Variance). This statistical test is appropriate because it allows for the examination of the effects of two independent variables (Type of Organization and Gender) on a continuous dependent variable (average donations).

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A. Optgion C is correct. y = - 4/5x + 94

b. The distance that Maria would have covered from her house is given as 58 meters

The formula to use here is

y2 - y1 / x2 - x1

= 70 - 94 / 30 - 0

= - 24 / 30

divide through by 6

= - 4 / 5

y = - 4/5x + 94

When x = 45

y = - 4/5x + 94

= -4 / 5 * 45 + 94

y = 180 / 5 + 94

y = -36 + 94

y = 58

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The mean of the probability distribution of the random variable is 3

From the question, we have the following parameters that can be used in our computation:

The probability distribution

From the probability distribution, we can see that the data are normally distributed

This means that the mean, the median and the mode are equal

From the distribution of the random variable, we have the following readings

Hence. the calculated mean of the random variable is 3

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Answer:

The volume of the 'scooped' pyramid is approximately 26.6667 cubic units.

To find the volume of the 'scooped' pyramid, we first need to determine the area of its base. Since the cross-sectional area of the pyramid is x 4 at a distance x from the tip, we can assume that the area at the tip is zero. This means that the area of the base is 4 times the area at a distance of 5 from the tip (since the distance from tip to base is 5).

Therefore, the area of the base is 4x4 = 16 square units. To find the volume, we can use the formula for the volume of a pyramid, which is:

Volume = (1/3) x Base Area x Height

In this case, the height of the pyramid is 5 units. So, we can substitute the values we have:

Volume = (1/3) x 16 x 5

Volume = 26.6667 cubic units

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