Find the area of the shaded region. Use the pin button on the calculator. Round to the nearest whole number. 14 70°

Answer:

7 + 2i is the simplified form

Step-by-step explanation:

Answer:

There is a 8.11% of any randomly selected athlete to test positive.

Step-by-step explanation:

To make it easier, let's assume there are 100 athletes

5/100 have it

3/95 do not have it, but will test positive

1% of the 5 athletes who are using PEDs = 0.05%

This means that 4.95/100 will test positive

3/95 = 3.16%

(rounded, if you need a more precise answer, it's 3.1578947368421053)

3.16% + 4.95% = 8.11%

Answer:

Y = 10 I believe

Step-by-step explanation:

To solve this we need to find what number, times 6, equals 18 - it's fairly easy to find the answer is three.

So what, minus seven, equals 3?

Answer:

x = -8

Step-by-step explanation:

these angles are vertical, therefore they are equal

x + 78 = 70

x = -8

Answer:

sorry

Step-by-step explanation:

The calculated value of z = 2.80 > the Critical value of z = -1.645, we reject the null hypothesis.

Hence, the claim that the proportion is at least 34% is rejected.

Therefore, the researcher's claim is true that the figure is higher for fathers in Littleton.

Null hypothesis:

H0: p ≥ 0.34

Alternative hypothesis:

Ha: p < 0.34

where:p = proportion of Littleton fathers who do not help with childcare

Here, the level of significance, α = 0.05

Level of significance = α = 0.05

The test statistics for a proportion is given as z-test.  

The formula for calculating z-score for a proportion is: z = (p - P) / sqrt[P(1 - P) / n]

where:

P = Population proportion

p = Sample proportion

n = Sample size

The calculated value of z-statistics can be compared with the critical value of z-score from the standard normal distribution table at a particular level of significance.

If the calculated value of z is greater than the critical value of z, then we reject the null hypothesis; otherwise, we fail to reject the null hypothesis.

Calculating the z-statistic:

Here, Sample size n = 225

Sample proportion

p = 97/225

= 0.4311

Population proportion

P = 0.34z

= (p - P) / sqrt[P(1 - P) / n]z

= (0.4311 - 0.34) / sqrt[(0.34)(0.66) / 225]z

= 2.80

Since the alternative hypothesis is one-tailed (Ha: p < 0.34), the critical value of z at α = 0.05 can be found as follows:

The critical value of z = zα

= -1.645

Since the calculated value of z = 2.80 > the Critical value of z = -1.645, we reject the null hypothesis.

Hence, the claim that the proportion is at least 34% is rejected.

Therefore, the researcher's claim is true that the figure is higher for fathers in Littleton.

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The function trapezium_integrator uses the Trapezium Rule to approximate the value of a definite integral. It takes four parameters: f, which represents the function to be integrated, start and end, which specify the interval of integration, and num_traps, the number of trapeziums used to approximate the integral.

In the Trapezium Rule, the interval of integration is divided into multiple trapeziums of equal width. The area of each trapezium is calculated as the sum of the lengths of its parallel sides divided by 2, multiplied by its height (the difference between the function values at the two endpoints of the trapezium). The areas of all trapeziums are then summed to obtain an approximation of the integral.

The function calculates the width of each trapezium by dividing the interval length by the number of trapeziums. It then uses a for loop to iterate over the trapeziums, accumulating their areas. The final result is returned as the approximation of the integral.

By dividing the interval into smaller trapeziums, the Trapezium Rule provides a reasonably accurate estimate of the integral. However, for highly oscillatory or irregular functions, a large number of trapeziums may be required to achieve sufficient accuracy. In such cases, more sophisticated numerical integration methods may be more appropriate.

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Answer:

soln,

3/5+7/8+3/10

3×8/5×8+7×5/8×5+3×4/10×4

24/40+35/40+12/40

24+35+12/40

71/40

The cross product of vector c and d is equal to (-4,9,13).

The cross product of two vectors a and b in three-dimensional space is a vector that is perpendicular to both a and b and thus normal to the plane containing them. Here, we have to find the cross product of two given vectors. The given vectors are:a = (3, -3, -2) and b = (3, -1, -2)  So, a x b = (4,0,6).The second one is given as: c = (1, -2,-2) and d=(-5,3,-1).

A vector in mathematics is a quantity that not only expresses magnitude but also motion or position of an object in relation to another point or object. Euclidean vector, geometric vector, and spatial vector are other names for it.

In mathematics, a vector's magnitude is defined as the length of a segment of a directed line, and the vector's direction is indicated by the angle at which the vector is inclined.

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Answer:

3x - 4 = 14

+4. +4

3x = 18

÷3. ÷3

x = 6

Step-by-step explanation:

the correct answer is exponent because 6 is its power.

hope this answer will help u.

have a great time.

The total area of the regions between the curves is 1/3 square units

The total cost for making the first 10 units is 7.79

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

y = √x and y = x²

With the use of graphs, the curves intersect at

x = 0 and x = 1

So, the area of the regions between the curves is

Area = ∫x² - √x

Integrate

[tex]Area = \frac{x^3}{3} - \frac{2x^\frac32}{3}[/tex]

Recall that x = 0 and x = 1

So, we have

[tex]Area = (\frac{0^3}{3} - \frac{2 * 0^\frac32}{3}) - (\frac{1^3}{3} - \frac{2 * 1^\frac32}{3} )[/tex]

Evaluate

Area =  0 + 1/3

Evaluate

Area =  1/3

Hence, the total area of the regions between the curves is 1/3 square units

Given that

MC(x) = √(x + 30)

Integrate to calculate the cost function

So, we have

[tex]C(x) = \frac{2}{3}(x + 30)^\frac23[/tex]

For the first 10, x = 10

So, we have

[tex]C(10) = \frac{2}{3}(10 + 30)^\frac23[/tex]

Evaluate

C(10) = 7.79

Hence, the total cost for making the first 10 units is 7.79

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Answer:

X= 4. Y = 3.

Step-by-step explanation:

If you have a graphing calculator, put the first equation into Y1. And second equation into Y2. Then, find the intersection of the two equations. That's how you find your answer.

There will be approximately 320 bacteria in 10 hours.

To determine the number of bacteria in 10 hours, we need to calculate the number of doubling cycles that occur within that time period.

Since the bacteria population doubles every 2 hours, in 10 hours, there will be 10/2 = 5 doubling cycles.

Starting with approximately 5 bacteria, each doubling cycle will result in the population doubling. Therefore, the number of bacteria after 5 doubling cycles can be calculated as follows

5 bacteria * (2^5) = 5 * 32 = 160

Hence, there will be approximately 160 bacteria after 5 doubling cycles or after 10 hours.

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Answer:

the yield is 7.68%

Step-by-step explanation:

The calculation of the yield is given  below:

we know that

Yield is

= Annual dividend ÷ Price per share

= $4.15 ÷ $54

= 7.68%

Here we divide the annual dividend by the price per share in order to determine the yield  

hence, the yield is 7.68%  

And, the same would be considered

Answer:

909.09

Step-by-step explanation:

400 divided by 44% is about 909.09.

Answer:

Range = [0, ∞)

Step-by-step explanation:

Range is the y-values

For this question y starts at 0 and just continually goes up so:

Range = [0, ∞)

Answer:

The range would be 50.

Step-by-step explanation:

Range is found by putting a list or set of numbers in order, then subtracting the lowest number from the highest.

32, 61, 63, 52, 82

82 - 32 = 50

Answer:

3 inches

Step-by-step explanation:

got it right on edg

Null hypothesis is that the median price of a round-trip ticket from Chicago, Illinois, to Jackson Hole, Wyoming is $605. Alternative hypothesis is that the median price of a round-trip ticket from Chicago, Illinois, to Jackson Hole, Wyoming is less than $605.

The median price of a round-trip ticket from Chicago, Illinois, to Jackson Hole, Wyoming is $605.

The median price of a round-trip ticket from Chicago, Illinois, to Jackson Hole, Wyoming is less than $605.As per the given question, Central Airlines claims that the median price of a round-trip ticket from Chicago, Illinois, to Jackson Hole, Wyoming is $605. But, this claim is being challenged by the Association of Travel Agents, who believes that the median price is less than $605. Hence, we need to test whether the median price is less than $605 or not.Hence,Null hypothesis is that the median price of a round-trip ticket from Chicago, Illinois, to Jackson Hole, Wyoming is $605. Alternative hypothesis is that the median price of a round-trip ticket from Chicago, Illinois, to Jackson Hole, Wyoming is less than $605.

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This is a binomial experiment because it meets all the necessary conditions: a fixed number of trials (20 people in the group), independence of each trial (assuming eye color is independent), two possible outcomes (green eyes or not), a constant probability of success (9% for having green eyes), and the random variable representing the count of successes (number of Americans in the group with green eyes, denoted as X).

To argue that this is a binomial experiment, we need to check if the following conditions are met:

1. The experiment consists of a fixed number of trials: In this case, we are considering a fixed number of trials equal to 20, as we have a random group of 20 American people.

2. Each trial is independent: We assume that the eye color of each person in the group is independent of the others.

3. There are only two possible outcomes for each trial: The outcomes are either having green eyes or not having green eyes.

4. The probability of success is constant for each trial: The probability of an American having green eyes is fixed at 9% for each individual in the population.

5. The random variable X represents the count of successes: In this case, X represents the number of Americans in the group with green eyes.

Since all these conditions are satisfied, we can conclude that this is a binomial experiment.

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The work required to pump the water out of the spout is 200000.64 J.

Given, Length of the tank = 8 m

Density of water = 1000 kg/m3

The work required to pump the water out of the spout can be calculated as follows:

Step 1: Consider a layer of water 'dx' thick at a height of 'x' meter above the bottom of the tank.

The volume of this layer is given by,V = Area × height= (8 × x) × dx= 8x dx

The mass of this layer is given by,m = density × volume= 1000 × 8x dx= 8000x dx

The force required to lift this layer of water is given by, F = mg= 8000x dx × 9.8= 78400x dx

Step 2: To find the work done, we need to multiply the force by the distance moved.

The distance moved by this layer of water is given by d, where d = (8 - x).

Therefore, the work done in moving this layer of water is given by, dW = F × d= 78400x dx × (8 - x)= 627200x dx - 78400x² dx

Step 3: The total work done in pumping out all the water is given by the integral of dW from x = 0 to x = 8.

That is,W = ∫dW = ∫₀⁸ (627200x dx - 78400x² dx)= [313600x² - 261333.33x³]₀⁸= 200000.64 J

Therefore, the work required to pump the water out of the spout is 200000.64 J.

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Answer: 92.5

So to find median all we have to do is rearange it from low - high and get the middle number

71  81  89  92  93  94  94  99

So take this and you know the middle is right in between 92 and 93

71  81  89  92      |      93  94  94  99

All you have to do then is take the mean of 92 and 93 (so do [92+93]/2)

Answer is 92.5

Answer:

y=3x

Step-by-step explanation:

Answer:

the answer is B)

4√24x^3/2x

Solution:

The answer is B

Step-by-step explanation:

I just took the test.

She decreased her candy intake by 15%

Methods to rid a model of multicollinearity include:  Include determination,Collection of data,Variable change,Regression ridge

Multicollinearity in a model can be addressed in a number of ways, including:

Include determination: Eliminating at least one connected factors can assist with mitigating multicollinearity. Stepwise regression or LASSO regression can be used to select the most important features by analyzing the correlation matrix.

Collection of data: By introducing variation in the predictors, more diverse and independent data can help reduce multicollinearity. Expanding test size can likewise help in relieving multicollinearity impacts.

Variable change: Multicollinearity can be reduced by transforming variables using centering, standardization, or power or logarithmic transformations. These changes change the scale or type of the factors, making them less associated.

Regression ridge: By adding a penalty term to the least squares estimation, ridge regression reduces the impact of multicollinearity and reduces the coefficients. This regularization method balances out the appraisals and works on model execution.

PCA, or Principal Component Analysis,: PCA can be used to generate uncorrelated linear combinations of the starting variables. These new factors, known as head parts, can be utilized as indicators in the model, successfully lessening multicollinearity.

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Answer:

Depends on the state you’re from

Step-by-step explanation:

Answer:

Yes you can! Grades from middle school don't matter when you get to high school. But try to get those grades up!

1.02 1.05 1.08 1.00 1.00 1.06 1.08 1.01 1.04 1.07 1,00 kg: To test the hypothesis, at 5% significance, that the mean weight for the whole batch is over 1.00 kg, we can perform a one-sample t-test. The null hypothesis (H0) assumes that the mean weight is 1.00 kg, while the alternative hypothesis (H1) assumes that the mean weight is greater than 1.00 kg. By calculating the sample mean, sample standard deviation, and the test statistic, we can compare it with the critical value to determine if there is sufficient evidence to support the alternative hypothesis.

7.4: To test the hypothesis, at 1% significance, that the mean lifetime of electric light bulbs has not changed, we can use a one-sample t-test. The null hypothesis (H0) assumes that the mean lifetime is 1500 hours, while the alternative hypothesis (H1) assumes that the mean lifetime is different from 1500 hours. By calculating the sample mean, known standard deviation, and the test statistic, we can compare it with the critical value to determine if there is enough evidence to reject the null hypothesis.

7.5: To test the hypothesis, at 5% significance, that the drug gives more hours of sleep on average than the control for all patients in the hospital, we can perform a one-sample t-test. The null hypothesis (H0) assumes that there is no difference in the mean hours of sleep between the drug and the control, while the alternative hypothesis (H1) assumes that the drug provides more hours of sleep on average. By calculating the sample mean, sample standard deviation, and the test statistic, we can compare it with the critical value to determine if there is sufficient evidence to support the alternative hypothesis.

7.6: To test the hypothesis, at 1% significance, that the coin is biased, we can use a binomial test. The null hypothesis (H0) assumes that the coin is unbiased (probability of heads = 0.5), while the alternative hypothesis (H1) assumes that the coin is biased. By calculating the number of heads observed, the expected number of heads under the null hypothesis, and the test statistic, we can compare it with the critical value to determine if there is enough evidence to reject the null hypothesis and conclude that the coin is indeed biased.

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