How To Do 387 x 72 In standard algorithm.​

The measure of the missing angles of the right triangle are 57 degree and 33 degrees.

The sum of the interior angles of a triangle equals 180 degrees.

From the diagram:

Angle A =  (4x - 7) degrees

Angle B = (3x - 15) degrees

Angle C = 90 degrees

In a right triangle, the sum of the two acute angles is 90 degrees.

So, we have:

angle A + angle B + 90 = 180 (sum of angles in a triangle)

(4x - 7) + (3x - 15) + 90 = 180

Solve for x

4x - 7 + 3x - 15 + 90 = 180

4x + 3x - 15 - 7 + 90 = 180

7x - 15 - 7 + 90 = 180

7x + 68 = 180

7x = 180 - 68

7x = 112

x = 112/7

x = 16

Therefore, the missing angles will be:

angle A = (4x - 7) =  4(16) - 7 = 57 degrees

angle B = (3x - 15) = 3(16) - 15 =  33 degrees.

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Answer:

The measure of the missing angles of the right triangle are 57 degree and 33 degrees.

Step-by-step explanation:

The option that would result in the smallest margin of error in estimating the mean salt content, u, is 95% confidence, n = 50. The correct answer is Option D.

The margin of error is the amount by which a statistic is expected to differ from the true value of the population parameter. The interval estimate is calculated with the help of a margin of error. The margin of error and the interval estimate are inversely related to each other. If we want a small margin of error, we must increase the sample size.

The confidence level is the likelihood that a population parameter will fall within a specified range of values. The confidence level is determined by the sample size and margin of error. The sample size and margin of error are directly related to each other. When the sample size is smaller, the margin of error is larger. When the sample size is larger, the margin of error is smaller.

The margin of error is the highest at a confidence level of 50%. In general, as the confidence level increases, the margin of error decreases, and vice versa. As the sample size increases, the margin of error decreases. It follows that a 95% confidence level, n = 50 would yield the smallest margin of error in estimating the mean salt content, u. Hence, option D) 95% confidence, n = 50 would result in the smallest margin of error in estimating the mean salt content, u.

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Answer: 12 cups

Step-by-step explanation:

Answer:

12 cups

Step-by-step explanation:

1 pint = 2 cups

6 pints = 6 × 2 cups = 12 cups

Answer:

To calculate the slope of the line that passes through the points (1, 2) and (-3, 5), we use the slope formula:

m = (Y2 - Y1) / (X2 - X1)

where (X1, Y1) = (1, 2) and (X2, Y2) = (-3, 5).

Substituting the values into the formula, we get:

m = (5 - 2) / (-3 - 1)

m = 3 / (-4)

Simplifying the fraction by dividing both the numerator and denominator by their greatest common factor of 1, we get:

m = -3/4

Therefore, the slope of the line in simplest form is -3/4.

The Airbus A320 Sullenberger was piloting typically cruises at about 550 miles, or 807 ft per minute. His greatest horizontal range would have been between 4.4 and 6.0 nautical miles.

In certain circumstances, you may trade your miles for items like cash back and gift cards in addition to using them to pay for flights. Most of the time, redeeming airline points is straightforward. By entering into your account while making your reservation with most flights, you may use your points to get a free ticket.

You may also determine distance in miles unless you already know your speed and how long it takes to go a particular distance. Just double the time by the speed.

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Answer: 64x^6y^15

Step-by-step explanation:

If the packaging box is in the shape of a cube, then all its sides are equal in length. Let's call the length of each side "s".

We know that the length of the packaging box is 4x^2 y^5, so:

s = 4x^2 y^5

To find the volume of the packaging box, we need to calculate s^3 (since the box is a cube).

s^3 = (4x^2 y^5)^3

s^3 = 4^3 (x^2)^3 (y^5)^3

s^3 = 64x^6 y^15

Therefore, the volume of the packaging box in terms of x and y is 64x^6 y^15.

Penelope jogs a distance of 2898 meters in three laps around the rectangular playground.

To solve this problem, we need to first find the perimeter of the rectangle. For a rectangle with a length of L and a width of W, the perimeter P can be found using the following formula:

P = 2L + 2W

In this case, the length of the playground is 320 m and the width is 163 m. So, the perimeter of the playground is:

P = 2(320 m) + 2(163 m)

P = 966 m

This means that if Penelope jogs around the entire playground once, she will run a distance of 966 meters.

Since Penelope jogs three laps around the playground, we need to multiply the perimeter by 3 to find out how far she runs in total. So, the distance that Penelope jogs in three laps is:

Distance = 3 x P

Distance = 3 x 966 m

Distance = 2898 m

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Complete Question:

Every day, Penelope jogs three laps around the playground to keep in shape. The playground is rectangular with a width of 163 m and a length of 320 m.

Determine how many meters Penelope jogs in three laps.

The quadratic equation x² + 2 - 4x = -4 in standard form is  x² - 4x + 6 = 0

A quadratic equation in standard form is given by ax² + bx + c = 0

Now, given the equation x² + 2 - 4x = -4, we desire to write it in standard form, we proceed as follows.

Since we have the quadratic equation x² + 2 - 4x = -4 writing it in standard form, we have

x² + 2 - 4x = -4

Adding 4 to both sides of the equation, we have that

x² + 2 - 4x + 4 = -4 + 4

x² + 2 + 4 - 4x = 0

x² + 6 - 4x = 0

Re-arranging, we have that

x² - 4x + 6 = 0

So, in equation in standard form is  x² - 4x + 6 = 0

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HAVE A NICE DAY !

Answer: 81 ham subs

Step-by-step explanation:

4 times 9 is 36. That means you have to multiply 9 by 9, to get 81 ham subs. The ratio is 81:36. :)

Answer:

The one selected is correct

Step-by-step explanation:

We select H1: μ ≠ 10, we will estimate the true mean of the target population 10 outside of the confidence interval.

p-value = 0.025

Confidence interval = 95% = 0.95

Level of significance (α) = 1 - Confidence interval

Level of significance (α) = 1- 0.95

Level of significance (α) = 0.05

However we are aware that the null hypothesis is rejected if the p-value is less than 0.05.

Due to the fact that our p-value (0.025) is below the α, we may thus reject our null hypothesis with α level of significance.

We choose H1: μ ≠ 10, the true mean of the target population 10 will be estimated outside of the confidence interval using the same data.

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The complete question is:

Suppose we do the below hypothesis test for the mean and obtained a p value 0.025.

H(0): μ = 10 Vs. H1: μ ≠ 10

If 95% confidence interval for the true mean of the target population is calculated using the same data, will 10 be inside or outside the confidence interval? Explain.

Compared to smaller firms, larger companies tend to option (B) systematically scan foreign markets to see where export opportunities lie.

Larger companies tend to systematically scan foreign markets to identify potential export opportunities due to their greater resources, including financial and human capital. This allows them to invest in researching and analyzing foreign markets, and to create effective exporting strategies. Furthermore, larger companies often have established distribution networks and greater brand recognition, which can make exporting a more viable option.

In contrast, smaller firms may lack the necessary resources and expertise to effectively navigate the complexities of exporting to foreign countries, and may therefore be more hesitant to seek out export opportunities.

Therefore, the correct option is (B) systematically scan foreign markets to see where export opportunities lie.

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The given question is incomplete, the complete question is:

Compared to smaller firms, larger companies tend to

Multiple Choice

A) delay exporting until after their domestic market is fully saturated.

B) systematically scan foreign markets to see where export opportunities lie.

C) be intimidated by the complexities and mechanics of exporting to foreign countries.

D) hesitate to seek export opportunities because they do not know how big the opportunities actually are

E) be reactive about seeking opportunities for profitable exporting.

A = 40,000(1 + 0.07)^3

A = 40,000(1.07)^3

A = 47,676.08

Therefore, the population of the town in 3 years will be approximately 47,676.

Karla paid 57.1% of the original price for an item that was originally priced at $350.

The explanation is that we can find the percentage that Karla paid by dividing the amount she paid by the original price and then multiplying by 100.

Percentage is a fraction out of 100. The formula for finding the percentage is as follows:  (part / whole) × 100.

So, if Karla paid $200 for an item that was originally priced at $350, we can find the percentage she paid as follows: (200/350) × 100 = 57.1%.

Therefore, Karla paid 57.1% of the original price. To round it to the nearest tenth of a percent, we can simply round the answer to one decimal place, which is 57.1%.

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Hi so you have to do grid for the equation

The probability of winning is 1/42 expressed as a fraction.

Given that the odds on a bet are 41:1 against, we are to find the probability of winning.

We know that odds against = (Number of unfavorable outcomes) : (Number of favorable outcomes).

Thus, odds against = 41:1. This implies that the number of unfavorable outcomes is 41 and the number of favorable outcomes is 1.Probability of winning is given by the formula P(winning) = Number of favorable outcomes / Total number of possible outcomes. In this case, the total number of possible outcomes is the sum of the number of favorable and unfavorable outcomes.

Number of favorable outcomes = 1 and Number of unfavorable outcomes = 41

Therefore, the total number of possible outcomes = 1 + 41 = 42

Thus, P(winning) = Number of favorable outcomes / Total number of possible outcomes

P(winning) = 1 / 42

Therefore, the probability of winning is 1/42 expressed as a fraction.

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Answer:

Step-by-step explanation:

All you have to do is subtract 6 from 14. The answer is 8. If the question is something like this one, always take the remainder and subtract it from how many you had in the beginning to get the answer.

Good luck

Peyton

The probability that the student will finish her trip in 60 minutes or less is 0.4

To find the probability that the student will finish her trip in 60 minutes or less, we need to find the proportion of the total area under the uniform distribution curve that lies to the left of 60 minutes.

Since the travel time is uniformly distributed between 40 and 90 minutes, the probability density function is

f(x) = 1/(90-40) = 1/50, for 40 ≤ x ≤ 90

The probability of finishing the trip in 60 minutes or less is therefore

P(X ≤ 60) = ∫[40, 60] f(x) dx

= (60-40)/50

Do the arithmetic operations

= 0.4

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Answer: 60%

Step-by-step explanation:

As mentioned earlier, we need to know the number of senior nutrition majors and sophomore non-nutrition majors to calculate the exact probability. Without this information, we cannot provide a specific answer to the question.

However, we can provide a general formula for computing the probability, assuming that the selections are made without replacement and the population of students is large enough to assume independence:

Probability of selecting a senior nutrition major and then a sophomore non-nutrition major = (Number of senior nutrition majors / Total number of students) × (Number of sophomore non-nutrition majors / (Total number of students - 1))

Once we have the values for the number of senior nutrition majors and sophomore non-nutrition majors, we can substitute them into this formula to obtain the probability, which will be a decimal number rounded to four decimal places.

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Theprobability of the employee arriving between 8:30 a.m. and 8:35 a.m. is 0.5 or 1/2.

The probability that the employee will arrive between 8:30 a.m. and 8:35 a.m. has to be determined. The given data is that the particular employee arrives at work sometime between 8:00 a.m. and 8:50 a.m.

Based on past experience the company has determined that the employee is equally likely to arrive at any time between 8:00 a.m. and 8:50 a.m.To find the probability that the employee will arrive between 8:30 a.m. and 8:35 a.m.

Using the above data, the probability that the employee will arrive between 8:00 a.m. and 8:50 a.m. is one. Therefore, the probability that the employee will arrive between 8:00 a.m. and 8:30 a.m. is 0.5, and the probability that the employee will arrive between 8:35 a.m. and 8:50 a.m. is 0.5.

The probability that the employee will arrive between 8:30 a.m. and 8:35 a.m. is also equally likely, so its probability is 0.5.

Therefore, the probability that the employee will arrive between 8:30 a.m. and 8:35 a.m. is 0.5 or 0.5 or 1/2.

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The mode of this data set is 3 inches.

In the given data set, the values of rainfall in inches for each week are as follows:

Week 1: 2 inches

Week 2: 3 inches

Week 3: 1 inch

Week 4: 3 inches

Week 5: 5 inches

Week 6: 4 inches

The mode is the value that appears most frequently, which in this case is 3 inches. Therefore, the mode of this data set is 3 inches.

Note that a data set can have more than one mode if there are two or more values that appear with the same frequency and are more frequent than any other value in the data set.

However, in this data set, there is only one mode, which is 3 inches.

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Answer: 33.33%

Step-by-step explanation:

5/15 divided

the test statistic (1.58) is greater than the critical value for a right-tailed test with α = 0.05.

To test the hypothesis H0: μ ≤ 50 vs Ha: μ > 50 at a significance level of α = 0.05, we can use a one-tailed z-test.

a) For the sample result x = 52.7, the test statistic is given by:

z = (x - μ) / (σ / [tex]\sqrt{n}[/tex]) = (52.7 - 50) / (8 / [tex]\sqrt{60}[/tex] ) = 2.38

To find the critical values for the rejection rule, we need to determine the z-value that corresponds to a right-tail probability of 0.05. Since we are using a one-tailed test, we can look up the z-value in the standard normal distribution table. The critical value for a right-tailed test with α = 0.05 is:

zα = 1.645

Since the test statistic (2.38) is greater than the critical value (1.645), we reject the null hypothesis and conclude that there is sufficient evidence to support the alternative hypothesis at a significance level of α = 0.05.

b) For the sample result x = 51, the test statistic is given by:

z = (x - μ) / (σ /[tex]\sqrt{n}[/tex] ) = (51 - 50) / (8 / [tex]\sqrt{60}[/tex] ) = 0.53

Since the test statistic (0.53) is less than the critical value for a right-tailed test with α = 0.05, we fail to reject the null hypothesis and conclude that there is insufficient evidence to support the alternative hypothesis at a significance level of α = 0.05.

c) For the sample result x = 51.8, the test statistic is given by:

z = (x - μ) / (σ / [tex]\sqrt{n}[/tex] ) = (51.8 - 50) / (8 / [tex]\sqrt{60}[/tex]) = 1.58

Since the test statistic (1.58) is greater than the critical value for a right-tailed test with α = 0.05, we reject the null hypothesis and conclude that there is sufficient evidence to support the alternative hypothesis at a significance level of α = 0.05.

In summary, the test statistic represents the number of standard deviations between the sample mean and the hypothesized population mean, and the critical values represent the cutoff points for rejecting the null hypothesis. The decision to reject or fail to reject the null hypothesis depends on whether the test statistic falls within or outside the critical values for the given level of significance.

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Therefore , the solution of the given problem of mean comes out to be one team's data values are closer to the median mean than that of the other.

The result from a set, also known as the arithmetic mean, is the sum of all values split by all of the values. It is frequently referred to as "mean" and is thought to be among the most commonly employed primary trend average. To obtain this answer, multiply the total number of numbers in the collection by the total number of values present.

Here,

The closer the data values are to the mean, the less variable they are, and the smaller the MAD.

A higher MAD shows that the data values are more variable and dispersed from the mean.

In this instance, the Bears' MAD of the amount of victories is 1, while the Saints' MAD is unspecified.

We cannot compare the distinctions of the two teams based solely on MAD since the Saints' MAD is not stated.

Since we cannot tell which team has a larger or smaller MAD, we cannot draw the conclusion that one team's data values are closer to the median mean than that of the other.

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Answer:

The correct answer is 21 km.

Step-by-step explanation:

Because

A = (1/2) · (p + q) · h

Where p is 9 and q is 5 (the bases)

and h is the height (3)

Then:

A = (1/2) · (p + q) · h

A = (1/2) · (9 + 5) · 3

A = (1/2) · (14) · 3

A = (1/2) · 42

A= 21

Brainliest Please!

answer: 21

7 times 3 = 21

To simulate the minutes between customer arrivals in Excel, one would use the Exponential distribution function in Excel.

This function is part of the Statistical functions category and can be found under the "Statistical" tab in Excel.

An exponential distribution is a probability distribution that describes the time between events in a Poisson process, where events occur continuously and independently at a constant average rate.

The Exponential distribution function in Excel takes two arguments: Lambda and x.

Lambda is the rate parameter, which describes the average number of events per unit of time.

In this case, the rate is 6 customers per hour, so Lambda would be equal to 6.

X is the time interval, and in this case, it would be the time between customer arrivals in minutes.

Therefore, to simulate the minutes between customer arrivals, one would use the following formula in Excel: =EXPON.DIST(x/60,1/6,TRUE)

The "x/60" part of the formula is used to convert the time interval from minutes to hours, as the rate is given in customers per hour.

The "1/6" part of the formula is used to set the Lambda value to 6.

The "TRUE" part of the formula is used to specify that we want the cumulative distribution function (CDF), which gives the probability that the time between events is less than or equal to x.

To simulate the minutes between customer arrivals in Excel, we would use the Exponential distribution function with Lambda = 6 and x being the time interval between customer arrivals in minutes.

The formula would be = EXPON.DIST(x/60,1/6,TRUE).

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